a psychophysical approach for predicting isometric … · a psychophysical approach for predicting...

A PSYCHOPHYSICAL APPROACH FOR PREDICTING ISOMETRIC AND

ISOTONIC HAND MUSCLE STRENGTH IN THE AVIATION INDUSTRY

BY

HESHAM A. ALMOMANI

BS, Yarmouk University, 1988

MSA, Central Michigan University, 2005

MAS, Embry-Riddle Aeronautical University, 2007

DISSERTATION

Submitted in partial fulfillment of the requirements for

the degree of Doctor of Philosophy in Industrial & Systems Engineering

in the Graduate School of

Binghamton University

State University of New York

2015

iii

Accepted in partial fulfillment of the requirements for

the degree of Doctor of Philosophy in Industrial & Systems Engineering

in the Graduate School of

Binghamton University

State University of New York

2015

November 20, 2015

Dr. Mohammad T. Khasawneh, Committee Chair and Faculty Advisor

Department of Systems Science & Industrial Engineering, Binghamton University

Dr. Krishnaswami "Hari" Srihari, Comittee Member

Thomas J. Watson School of Engineering and Applied Science, Binghamton University

Dr. Nagen Nagarur, Committee Member


Dr. Harold W. Lewis III, Committee Member


Dr. Roy T.R. McGrann, Outside Examiner

Department of Mechanical Engineering, Binghamton University

iv

ABSTRACT

In the aviation industry, most operations are accomplished using hands. Hand grip

strength is a significant factor that can influence human performance in terms of the

amount of force that an individual can apply and their time endurance limit. The main

objective of this study is to determine the maximum voluntary contraction and fatigue

endurance limits for both types of hand muscles (isometric and isotonic) for workers in

the Jordanian aviation industry. Using a psychophysical approach based on human

subjective perception of fatigue, a total number of 132 (aged between 20 and 60 years

old) subjects from the aviation industry was studied. The experiment investigates the

effect of nine different factors on three responses: maximum voluntary contraction

(MVC), isometric endurance limit, and isotonic endurance limit, and the relationships

between them. In addition, general and specific predictive linear models were developed

where not all factors are included simultaneously. The predictor variables are age, hand

dominancy, human body posture, grip circumference (GC), forearm circumference (FAC),

body mass index (BMI), height, profession (trade) and smoking condition. The isometric

endurance limit tested for different percentages of MVC at 20%, 40%, 60% and 80%,

which reflects real-life situations. The isometric endurance limit was tested for those

between 20% and 60% of the MVC force. In this experiment, digital hand grip

dynamometer was used to increase the accuracy of the experiment. The research

experiment outputs were analyzed with statistical analysis (e.g., descriptive statistical

analysis, interval plots, model adequacy checks, residual plots, MANOVA and ANOVA).

v

Mathematical modeling (linear and nonlinear) and machine learning techniques

(Artificial Neural Networks (ANNs), Artificial Neuro Fuzzy Inference System (ANFIS))

were applied. Results show that age and physical factors have significant effects. All

predictive models compared on the R-squared values and Root Mean Square Error

(RMSE). The machine learning models obtained the lowest RMSE (7.09 e -8 - 9.9 e-1)

and provided the better fit for the data than the mathematical models, especially ANFIS

methodology; however, linear models were convenient to build for this research. A pilot

study was conducted to refine the best framework for the actual experiment. Research

findings can be applied to the employment process of aviation industry workers as well as

to workers of police, firefighting, and air force to enhance general health of athletic

personnel and for better design tasks and related tools in a more economical way.

vi

DEDICATION

In the name of Allah, the most beneficent, the most merciful, this dissertation is

dedicated to the following people: First my father (رحمه هللا), my mother, wife and

my family for their endless encouragement, love and support, without their prayers,

I would never have gotten to this stage of academic development. Second for those

nation figures, distinctive, unequaled MEN, Major Generals Basha’s his excellency

Atif Altel, Engineer Faith Zael Bani Saker, Pilot Mohamad Alomari, Pilot Hilal

Faraj Alnajar, Judge Ziad Edwan and Pilot Hashim Al-momani), ex-senator

Samih Al-momani, dearest friends Engineer Hasan Mobideen, Naser Batayneh

and Mwafaq Alzobi, and finally the Hughes 203 team, All instilled me with the

strength, values, principles, and discipline with which to succeed in any task big or

small and who have always believed in me and inspire me to be who I am today.

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ACKNOWLEDGEMENTS

I wish to thank Dr. Mohammad T. Khasawneh, who never lost his patience with me

during the very difficult time in the last seven years for his guidance and support

throughout my study and research. I am also exceedingly grateful to Professor

Khasawneh and Vice Provost for International Affairs, Dean and University

Distinguished Professor Krishnaswami "Hari” Srihari who both inspired me to be what I

am today. My appreciation is also extended to Professors Nagen Nagarur and Harold W.

Lewis III for their kindness and help during my study. Special thanks go to all my friends

at Royal Jordanian Air Force (officers and NCOS) who have assisted me in my study and

experimentations. I am exceedingly grateful to Professor Mohammad T. Khasawneh for

his guidance and support throughout my whole doctoral program. As a mentor, his

dedication to his students is unsurpassed.

viii

Table of Contents

Section Page

List of Tables viii

List of Figures xii

Chapter One Introduction 1

1.1 Work Related Musculoskeletal Disorders 1

1.2 Human Muscle Fatigue 8

1.3 Human Grip Strength 15

1.4 Maximum Voluntary Contraction 17

1.5 Problem Statement 23

1.6 Research Objectives 24

1.7 Research Significance 27

1.8 Dissertation Organization 28

Chapter Two Literature Review 29


2.2 Isometric Endurance Limit 39

2.3 Isotonic Muscle Fatigue 52

2.4 Isokinetic Muscle Fatigue 53

2.5 Grip Strength New Research Areas 55

Chapter Three Research Methodology 60

3.1 Introduction 60

3.2 Experiment Elements 60

3.3 Experimental Procedure 64

3.4 Data Modeling and Analysis 67

Chapter Four Analysis and Discussion 68

4.1 Introduction 68

4.2 Descriptive Statistics 70

4.3 Multivariate Analysis Of Variance (MANOVA) 71

4.4 Basic Analysis 74


4.6 Isometric Endurance Limit 97

4.7 Isotonic Endurance Limit 125

4.8 Neural Network Analysis 142

4.9 ANFIS Neural Network Analysis 153

Chapter Five Conclusions and Future Work 156

5.1 Mathematical Modeling Conclusion 156

5.2 Neural Network Analysis Conclusion 175

5.3 ANFIS Neural Network Analysis Conclusion 176

5.4 Future Work 177

Appendices 179

References 193

ix

LIST OF TABLES

Page

Table 1-1 Independent Variables 25

Table 2-1 Grip Strength Value For Middle Aged Females 34

Table 2-2 2 Maximum voluntary for Standing and Sitting and Dominant Hand 37

Table 2-3 MVC Regression Models for MVC 38

Table 2-4 MVC Fractions with Wrist Posture Effect 43

Table 3-1 Descriptive Statistics of Aviation Male Subjects 60

Table 3-2 Dependent and Independent Variables and Treatment levels 61

Table 3-3 Overall Research Methodology for Aviation Subjects 63

Table 3-4 Data Analysis and Modeling Methodology 67

Table 4-1 Dependent and Independent Variables with Their Levels 68

Table 4-2 Overall Summary Data 70

Table 4-3 Descriptive Statistics (Dependent Factors) 71

Table 4-4 MANOVA for Experiment Terms 72

Table 4-5 MANOVA for all Dependent Factors 73

Table 4-6 Factor Information for ANOVA General Factorial Regression 76

Table 4-7 ANOVA General Factorial Regression 77

Table 4-8 MVC General Linear, Nonlinear Models (MATLAB 15) 79

Table 4-9 MVC General Linear Models (Detailed) (MATLAB 15) 80

Table 4-10 MVC General Non-Linear Models (detailed) (MATLAB 15) 84

Table 4-11 RMSE Values (Linear and Non-Linear) Regression 81

Table 4-12 MVC Values for Posture (Standing and Sitting) 82

Table 4-13 MVC Values for Strongest Age Periods 85

Table 4-14 Descriptive Statistics for Jordanian Subjects 92

Table 4-15 Descriptive Statistics: MVC Values for Different Races 92

Table 4-16 Factor information for ANOVA General Factorial Regression 97

Table 4-17 ANOVA General Factorial Regression: Isometric En 20% 99




Table 4-21 ANOVA Significant Factors 103

Table 4-22 ANOVA Interaction Factors 104

Table 4-23 Isometric Endurance Limit General Linear Models 105

Table 4-24 Isometric Endurance Limit Non Linear Regression 106

Table 4-25 Isometric Endurance Limit RMSE Values Linear and Non-linear Models 107

Table 4-26 Means for Isometric Endurance Limit for different Age groups 108

Table 4-27 Anthropometric Data for Jordanian Subjects 121

Table 4-28 Descriptive Statistics: Isometric End, Limit 122

Table 4-29 ANOVA General Factorial Regression 126

Table 4-30 Isotonic Endurance Limit General Linear and Nonlinear Models 127

Table 4-31 Isotonic Endurance Limit General Linear Models (MATLAB 15) 128

Table 4-32 Isotonic Endurance Limit General Nonlinear Models (MATLAB 15) 128

Table 4-33 RMSE Values Isotonic Endurance Limit Linear and Non-Linear regression 128

Table 4-34 Isotonic Endurance Limit Descriptive Statistics 129

Table 4-35 Summary Isotonic Endurance Limit Vs Age 129

Table 4-36 Summary of FAC Effect in Isotonic Endurance Limit Test 135

Table 4-37 Anthropometric Data 137

Table 4-38 General Linear Models for Isotonic Endurance Limit 137

x

Table 4-39 Nonlinear Regression Models for Isotonic Endurance Limit 138

Table 4-40 Summary of Neural Network Performance (MVC, Isometric and Isotonic

Endurance Limits)

143

Table 4-41 Neural Network Performance for MVC Test 144

Table 4-42 Neural Network Performance for Isometric Endurance Limit 144

Table 4-43 Neural Network Performance for Isotonic Endurance Limit 145

Table 4-44 Neural Network Performance for the Three Tests 146

Table 4-45 Neural Network Error Histogram 147

Table 4-46 Neural Network Function Fit Plot 149

Table 4-47 Neural Network Regression Plots for the Three Tests 151

Table 4-48 ANFIS Output Errors for the Three Tests (MVC, Isometric and Isotonic

Endurance Limits)

153

Table 4-49 ANFIS Output Errors for Each Experimental Condition 153

Table 5-1 General Linear and Nonlinear Models for MVC Test (MATLAB 15) 158

Table 5-2 Posture Effect on MVC 158

Table 5-3 Age Effect on MVC 159

Table 5-4 Height Effect on MVC 159

Table 5-5 BMI Effect on MVC 160

Table 5-6 Hand GRIP Circumference (HGC) Effect on MVC 160

Table 5-7 Forearm Circumference (HGC) Effect on MVC 160

Table 5-8 Trade Effect on MVC 161

Table 5-9 Race Effect on MVC 161

Table 5-10 Smoking Effect on MVC 162

Table 5-11 Dominancy Effect on MVC 162

Table 5-12 General Linear Models for Isometric Endurance Limit 163

Table 5-13 Isometric Endurance Limit Nonlinear Regression 164

Table 5-14 Age Effect on Isometric Endurance Limit 165

Table 5-15 Height Effect on Isometric Endurance Limit 165

Table 5-16 BMI Effect on Isometric Endurance Limit 166

Table 5-17 Hand Grip Circumference (HGC) Effect on Isometric Endurance Limit 166

Table 5-18 Forearm Grip Circumference (HGC) Effect on Isometric Endurance Limit 167

Table 5-19 TRADE Effect on Isometric Endurance Limit 167

Table 5-20 Isometric Endurance Limit for Jordanian Subjects 168

Table 5-21 Smoking Effect on Isometric Endurance Limit 168

Table 5-22 Hand Dominancy Effect on Isometric Endurance Limit 169

Table 5-23 Isotonic Endurance Limit General Linear and Nonlinear Model 169

Table 5-24 Age Effect on Isotonic Endurance Limit 170

Table 5-25 Height Effect on Isometric Endurance Limit 170

Table 5-26 BMI Effect on Isometric Endurance Limit 170

Table 5-27 Hand Grip Circumference (HGC) Effect on Isometric Endurance Limit 171

Table 5-28 Forearm Effect on Isometric Endurance Limit 171

Table 5-29 Trade Effect on Isometric Endurance Limit 172

Table 5-30 Isometric Endurance Limit for Jordanian Subjects 172

Table 5-31 Smoking Effect on Isometric Endurance Limit 173

Table 5-32 Hand Dominancy Effect on Isometric Endurance Limit 173

Table 5-33 Neural Network Summary (MVC, Isometric and Isotonic Endurance

Limits)

174

Table 5-34 ANFIS Output Errors for the Tests (MVC, Isometric and Isotonic

Endurance Limits)

175

Table 5-35 ANFIS Output Errors for Each Experimental Condition 175

xi

LIST OF FIGURES

Page

Figure 1-1 Carpal Tunnel Syndrome 3

Figure 1-2 Lateral Epicondylitis 4

Figure 1-3 Work Related Musculoskeletal Disorders 5

Figure 1-4 MSDs Injuries and Illnesses Numbers for Year 2010 (BLS, 2010) 6

Figure 1-5 QEC Assessment Form 7

Figure 1-6 Median Days Away From Work and Incidence Rate Due To

Injuries and Illness by Nature 2010 (BLS, 2010)

9

Figure 1-7 Number of Sprain, Strain, and Tear Cases Requiring Days Away

From Work by Selected Part of Body (BLS, 2010)

9

Figure 1-8 Average Days Away from Work 13

Figure 1-9 Muscles Involved in Grip Strength (Vansuh, 2012) 18

Figure 2-1 Males MVC with Age (Chatterjee & Chowdhuri, 1991) 41

Figure 2-2 Various Hand Wrist Postures Used (Khan, 2010) 42

Figure 2-3 Endurance Limit vs. MVC% (Different Shoulder Posture) 44

Figure 2-4 Endurance Limit for Different % of MVC 45

Figure 2-5 Endurance Limit Of 40% of MVC 46

Figure 2-6 Endurance Limit Of 40% of MVC of Left Hand and Right Hands 51

Figure 3-1 Experiment Instruments 62

Figure 3-2 Subject Posture during the Tests 64

Figure 4-1 Residuals Plots for MVC 78

Figure 4-2 MVC Models (Chatterjee & Chowdhuri, 1991) 80

Figure 4-3 MVC Posture effect (D) 83

Figure 4-4 MVC Posture effect (ND) 83

Figure 4-5 Relationship between MVC and Age for Different Posture and

Hand Dominancy

85

Figure 4-6 Relationship between MVC and Age 86

Figure 4-7 Relationship between MVC and Height 87

Figure 4-8 Relationship between MVC and BMI 88

Figure 4-9 Relationship between FAC and MVC 90

Figure 4-10 Relationship between Trade and MVC for Different Posture and

Dominancy

91

Figure 4-11 Relationship between MVC and Race (Male) 93

Figure 4-12 Relationship between MVC and Smoking 94

Figure 4-13 Relationship between Hand Dominancy and MVC for Different

Age Groups, Hand Dominancy, and Posture

95

Figure 4-14 Residual plots for isometric endurance limit test 105

Figure 4-15 Relationship between Isometric Endurance Limit and Age 108

Figure 4-16 Relationship between Isometric Endurance Limit and Height 111

Figure 4-17 Relationship between Isometric Endurance Limit and BMI 113

Figure 4-18 Relationship between Isometric Endurance Limit and HGC 115

Figure 4-19 Relationship between Isometric Endurance Limit and FAC 117

Figure 4-20 Relationship between Isometric Endurance Limit and Trade 119

Figure 4-21 Relationship between Isometric Endurance Limit and Smoking 122

Figure 4-22 Relationship between Isometric Endurance Limit and Dominancy 124

Figure 4-23 Residual Plots for Isotonic Endurance Limit 127

Figure 4-24 Relationship between Age and Isotonic Endurance Limit for

Different Speed and Dominancy

130

xii

Figure 4-25 Relationship between Height and Isotonic Endurance Limit 131

Figure 4-26 Relationship between Isotonic Endurance Limit and BMI 132

Figure 4-27 Relationship between Isotonic Endurance Limit and HGC 133

Figure 4-28 Relationship between Isotonic Endurance Limit and FAC 134

Figure 4-29 Relationship between Isotonic Endurance Limit and Trade for

Different Speeds and Dominancy

136

Figure 4-30 Relationship between Isotonic Endurance Limit and Smoking 139

Figure 4-31 Relationship between Isotonic Endurance Limit and Hand

Dominancy

140

Figure 4-32 General Neural Network Diagram 144

Figure 4-33 ANFIS Diagram 154

xiii

Intentionally Left Blank

1

CHAPTER ONE INTRODUCTION TO RESEARCH

In this chapter, research importance, motivation, significance investigations, relevance of

this research to ergonomics in aviation industry will be introduced, outlines problem

statement. Since Muscle strength and muscular endurance considered as major

components and indicators of human body fitness and these are associated with health.

1.1 WORK RELATED MUSCULOSKELETAL DISORDERS

Work related musculoskeletal disorders (WMSD), introduced in different names in the

world as (Cumulative Trauma Disorder (CTD), Work-related Upper Limb

Disorders(WRULDs), Repetitive Strain Injury (RSI), Upper Limb Disorder (ULD),

Occupational Cervicobrachial Disorder (OCD), Occupational Overuse Syndrome (OOS),

Musculoskeletal Disorder (MSD) in Great Britain, Canada, Australia, Holland, United

States, Japan, Scandinavia, Australia, New Zeeland and Holland. The most used name

worldwide is the Work-Related Musculoskeletal Disorder (WMSD), work related

musculoskeletal disorders, defined in different ways according to World Health

Organization (WHO, 1997) WMSDs are defined “as multi factorial where a number of

risk factors contribute significantly to their development and their risk factors are

classified as physical, work organizational, psychosocial, individual, or social-cultural”,

according to Canadian Centre for Occupational Health and Safety, Work Related

Musculoskeletal Disorders (WMSDs) defined as “a group of painful disorders exhibited

in body muscles, tendons, and nerves, WMSDs and associated muscular discomfort hurt

2

and pain, exhibited in muscles, tendons, and nerves”. According to the National Institute

of Occupational Safety and Health (NIOSH), WMSDs are “those diseases and injuries

that affect the musculoskeletal, peripheral nervous, and neurovascular systems that are

caused or aggravated by occupational exposure to ergonomic hazards”. According to the

WHO, they characterized “work-related” complaints as multi-factorial because of

surrounding work and multi-factorial nature, this enabled them from distinguishing the

risk factors that contributed to cause these diseases, these factors are individual

capabilities, physical limitations, work organizational policies, psychosocial, and socio-

cultural. According to the Canadian Centre for Occupational Health and Safety, these are

very difficult to characterize within the classification of traditional diseases. WMSDs

included repetitive motion and strain injuries, Cumulative trauma, Soft tissue, and

regional, occupational and overuse musculoskeletal disorders”. The NIOSH classified

and grouped WMSDs into four main groups based on distinct features:

1. Body parts, muscles, joints, nerves, and spinal cord injuries and discs.

2. Occasional disorders (events) such as fall or slips.

3. Intensity (intermittent/persistent), that based on historical body disorders

discovered in later medical checkups.

Special or distinctive disorders like (carpal tunnel syndrome), which is defined according

to the Canadian Centre for Occupational Health and Safety, as a common nerve

entrapment disorders that caused by long time intensity of work and or repetitive work,

according to Health and Safety Executive, they classified WMSDs based on risk factors

into four groups as follows: 1) Task-related factors, 2) Environment-related factors, 3)

Psychosocial factors, and 4) Worker-related factors. Other researchers like Fernandez and

3

Marley (2011) initiated the classification of WMSDs based upon the affected part of

human body, upper extremities like tendons disorders, Thoracic outlet syndrome,

Neurovascular disorders, Vibration syndrome, White finger syndrome, and Nerve

disorders), lower extremities diseases and low back pain that mainly caused by manual

material mishandling (MMM), like pulling, pushing, lifting, Hand-Arm vibration, etc.

Figure 1-1 and figure 1-2 provides examples of the work related musculoskeletal

disorders:

.

Figure 1-1 Carpal Tunnel Syndrome

4

Figure 1-2 Lateral Epicondylitis

As a summary, WMSDs resulted from abnormal conditions or human body physical

activities that included risk factors like doing a certain job repeatedly (repetition),

posture, extended duration, recovery time, extra repetitive motions, psychosocial

factors, excessive physical work, workload and pacing, extended use of human muscle,

hand-arm vibration, cold stress, uncomfortable awkward postures, force,

velocity/acceleration and mechanical stress, caused by or over a long period that exceeds

worker body limits. Some statistics were revealed about these injuries, according to the

United States Bureau of Labor Statistics (BLS, 2010) 40% injuries pertained to tears and

strains, with 36% pertains to back injuries, 26% pertains to lower body part extremities

and finally 12% for shoulders and hand injuries, where smallest portion pertained to

upper extremities (forearm and hand), as shown in Figure 1-3 (BLS, 2010).

5

Figure 1-3 Work Related Musculoskeletal Disorders, Non-Fatal (Centers for Disease

Control and Prevention (CDC, 2010)

Statistical research shows that WMSDs vary considerably from one job to another and

depend on gender. According to Jeong (2005), “they are widespread among the nursing

aides, attendants and healthcare workers such as sonographers” with higher rate in

females, followed by the freight, stock, and material movers workers. According to BLS

(2010), Figure 1-4 shows the number of Injuries due to WMSDs for particular

occupations, cost and risk associated with WMSDs.

6

Figure 1-4 MSDs Injuries and Illnesses (BLS, (2010)

Work related musculoskeletal disorders (WMSD), incurred industries high cost and in

most cases the precise cost is not known because of inaccurate estimates, since it includes

many costs, like workplace and medical costs. According to Davies and Teasdale (1994),

in Great Britain the overall cost of Work related musculoskeletal disorders (WMSD) that

includes (work-related illnesses beside avoidable accidents) “between £6 billion and £12

billion annually”. According to NIOSH (1997), WMSDs cost was around $13 billion in

the United States annually, while according to AFL-CIO (1997) had more estimate

exceeds $20 billion annually, overall and regardless of the assessment used, the problem

is large both in health and economic term (NIOSH,1997). However, David et al. (2008)

developed “Quick Exposure Check (QEC) for assessing exposure to risk factors for

work-related musculoskeletal disorders, which is an observational tool developed for

Occupational Safety and Health (OSH) practitioners to assess exposure to risks for work-

7

related musculoskeletal disorders and provide a basis for ergonomic interventions”, as

shown in Figure 1-5 (QEC Assessment Form).

Figure 1-5 QEC Assessment Form

8

1.2 HUMAN MUSCLE FATIGUE (DEFINITION and DESCRIPTION)

The prolonged, accumulated and repetitive job tasks can lead to adverse effects on the

human body parts and or muscles tissues like injuries and pain. Rohmert (1960, 1966)

defined human fatigue as a “periodic process in every living organism, and all organisms

are recoverable from fatigue by nature”, he also mentioned that fatigue can be recognized

by both the reduction in activities accompanied by feeling of fatigue. Edwards (1981)

defined fatigue as is “the failure to sustain the required job or task force, muscle fatigue

cause a reduction in the maximum voluntary contraction (MVC) and can be induced by

exercise. Fatigue can happen in both material, animals and human beings as response to

repeated or extra loads beyond their capabilities, material fatigue might lead to fracture of

the material, however, it is less harmful condition in humans but will reduce the strength

and performance of human body and mental awareness. Fatigue has a significant effect

on human performance. Snook and Irvine (1969) and Snook (1978) conducted

physiological and psychophysical fatigue experiments to measure the effect of fatigue on

performance, he stated that there is a significant relationship between performance and

psychological measures of fatigue and none consistent relationship between performance

and physiological measures of fatigue. Figure 1-6 shows median days away from work

and incidence rate due to injuries and illness by nature (BLS, 2010) and figure 1-7 shows

number of sprain, strain, and tear cases requiring days away from work by selected part

of body, industry (BLS, 2010).

9

Figure 1-6 Median Days Away From Work and Incidence Rate Due To Injuries and

Illness by Nature (BLS, 2010).

Figure 1-7 Number of Sprain, Strain, and Tear Cases Requiring Days Away From

Work by Selected Part of Body, Industry (BLS, 2010).

10

Human physical fatigue may be caused on both levels the main central nervous system

that drives the motoneurons and on the muscle level peripheral changes. Researchers

classify human fatigue into two types, the Physical Fatigue and Mental Fatigue as

follows: 1) Physical or muscular fatigue happens when the human body muscles fails to

sustain and utilize any extra amount of loads and exert forces for defined job. Physical

muscle fatigue, also defined as the decline in the human muscle strength that lead to

reduction in ability to produce muscle force. According to Vollestad (1997) and Chaffin

et al. (1999), this type of fatigue resulted in reduction in the capacity or ability to exert

and generate any extra force to any new voluntary effort, this research will explore hand

grip limitations that lead to Physical fatigue; 2) Mental fatigue happens when the human

body attention or level of consciousness reduced for any reason, according to Baumeister

(2002) “Mental Fatigue could lead to reduction in human memory, wrong or late

decision, causing sleeping problems, etc.”.

11

Ergonomics researchers classified and typed human muscle fatigue according to the

(motor pathways) connection means between brain and muscles as either central fatigue

or Supraspinal fatigue: 1) Central fatigue, where the body has a general feelings of

tiredness, weakness and exhaustion according to Taylor et al. (2005) the Central fatigue

defined as a “progressive exercise-induced reduction in voluntary activation or neural

drive to the muscle;” 2) Supraspinal Fatigue, Where a specific part of the body has

feelings of tiredness, weakness and exhaustion, recognized as Localized Muscle Fatigue

(LMF). The LMF caused a reduction in muscle strength and it’s a job time dependent.

Hainaut (1989) stated that the Localized Muscle Fatigue (LMF) happened when human

muscle cannot maintain the necessary force level due to decrease in the amount of

generated muscle tension. According to Taylor et al. (2005) supraspinal fatigue defined

as “an exercise-induced decline in force due to suboptimal output from the motor cortex”.

Blackwell et al. (1999) mentioned that the Localized Muscle Fatigue LMF is the

incapability of a muscle to keep the required job force. Edwards (1981) mentioned that

“maximum voluntary contraction (MVC) is graded according to tension generated

together with the number of fibers recruited, it can be attributed to failure of rate of

energy to meet the demand”. According to Gandevia (2001) “spinal and supraspinal

factors in human muscle fatigue, stated that MVC in most cases are less than the actual

maximal muscle force”. The human physical fatigue rate increased in heavy loads over

short time job tasks or small load over an extended period of time besides the repetitive

tasks and directly proportional with the amount of load force, load exertion time, and

abnormal postures and inversely proportional with rest time. According to Kumar and

Fagarasanu (2003) the great amount of forces do not necessarily be the primer cause of

12

muscle fibers injuries, and he emphasized that a repetitive low muscular force might

cause injuries to the human muscles. Also according to Sjogaard et al. (2000) a

continuous recruited muscles fibers because of an impairment in the local muscle

metabolism that become deleterious after repeating the same recruitment pattern. The

causes of physical fatigue in human or material depends on their specifications

capabilities and limitations together with many other factors like (task, environment,

psychosocial and Worker-related) factors that includes (doing a certain job repeatedly

(repetition), posture, extended duration, recovery time, extra repetitive motions,

psychosocial factors, excessive physical work, Workload and pacing, extended use of

human muscle, hand-arm vibration, cold stress, uncomfortable awkward postures, force,

velocity/acceleration and mechanical stress caused by or over a long period that exceeds

worker body limits. According to Chaffin et al. (1999), awkward postures dramatically

increase speed of fatigue occurrences, researchers also studied the posture effect like

Sjogaard et al. (2000) who found out that abnormal awkward postures cause higher

fatigue than normal neutral postures which cause lesser fatigue. According to BLS (2010),

Figure 1-8 shows the nonfatal injuries and median days away from work rates.

13

Figure 1-8 Average days away from Work due to Repetitive Motion in

Comparison to all other nonfatal Injuries (BLS, 2011)

From physical point view the muscle fatigue can be controlled through different means

through controlling the exerted force volume, job total repetitions, job durations,

postures and rest periods, taking into considerations that the fatigue feelings start when

the above factors limit exceeds the human muscle limitations and capabilities, and these

factors have an effect on each other where working under normal postures still exposed to

physical fatigue in long periods jobs and increased rate of fatigue happened in the

awkward posture, all of the above factors also resulted in human muscles pain and body

parts complaints and disorders, and this is a very important factor where the job design

the job resting periods to decrease the muscle fatigue occupancies. Researchers and job

designers always look for the best reliable methods to measure the fatigue critical point

since it is different according to many factors like (task, environment, psychosocial and

worker-related factors besides the occurrence nature. Ergonomics fatigue experts usually

use the following approaches to find out the fatigue limitations: 1) the physiological

approach, where in this approach researchers measure the human body heart rate, oxygen

14

intake rate, and amount of energy expenditure. These measures help them in job and

different tasks design within acceptable limits. According to Dempsey (1998),

physiological approach responses used to insure that human body doing the jobs within

acceptable limits; 2) The psychophysical approach, in this approach depends on human

subject judgment and rating of stress and strain on their joints and muscles, some

researchers like Snook (1978) offer a surveying standard tool that can be used to measure

the psychophysical assessment. According to Snook (1978), the psychophysical approach

include the individual subjective rating to evaluate the fatigue of different body parts

muscles and joints; 3) The biomechanical approach, in this approach, according to

Jorgensen et al. (1999), the researchers use the mechanics principles to measure body

parts moments, against human physical structure, like torque, shear forces, compression

rate on (joint, spines), according to Jorgensen (1999), in the biomechanical approach the

mechanic principles used to evaluate fatigue limits through the measure of tensile, shear

and compression, moment and torques on body parts of the human body.

15

1.3 HUMAN GRIP STRENGTH

In order to study general body strength from all aspects, ergonomics researchers divided

the human body muscles strength into three type’s isometric, isotonic, and isokinetic,

when exposed to fatigue these muscles strength will be reduced: 1) Isometric Muscle

Strength: Chaffin (1975) defined the isometric muscle strength: as the “capacity to

produce torque or force by a maximal voluntary isometric muscular exertion”. Jackson

(1994) defined it as the “ability to exert maximum force without 10% of the body

strength as stated by Rohmert (1966); 2) Isotonic Muscle Strength: In the Isotonic Muscle

Strength the muscle length changed in none constant speed during movement of the body

parts. Knapik et al. (1983) defined the Isotonic Muscle Strength as the “capacity to

produce torque or force while the muscle changes length during contraction and cause

movement of the body part”. TeachPe Team (2012) classified isotonic muscle strength

into two types depending on the length of muscle: A) Eccentric Isotonic Muscle Strength,

where muscle length extended during the contraction; and B) Concentric Isotonic Muscle

Strength, where muscle length shortened during the contraction; 3) Isokinetic Muscle

Strength: In the Isokinetic Muscle Strength, the muscle changes its length in constant

rate/ manner, Jackson (1986) defined the Isokinetic Muscle Strength as the “the ability to

exert maximum force with producing movement”.

Hand grip is one of the first most used body parts, hand grip does not act by itself it is

related to hand muscles strength. According to Gonzalez et al. (1997) the hand forearm

and hand 35 different muscles working together to achieve the necessary movement, the

hand grip strength used as an indicator of the upper body general strength, and its

assessments found useful in evaluating the advancement of patients that are undertaking

16

physical therapy. According to Poitras (2011) hand grip strength can be used as a very

important screening tool in evaluating a human overall health, hence, he used in his

research the hand grip strength as reference indicator of the human muscle mass to find

out and predict future events such as "post-operative complications". Hand grip strength

readings helped nutritional experts and health practitioners in their jobs to prescribe and

design the body exercises, nutritional strategies and other interventions to improve the

human overall health and vitality. According to Stafford et al. (1989) the hand grip

strength measurements, especially the maximum grip strength are used by many

researchers to use different body measurements, hand and hand grip used in many human

activities and sports, can be used in altered postures to accommodate the task nature.

According to Koley et al. (2009) grip strength defined as the “force applied by the hand

to pull on or suspend from objects and is a specific part of hand strength”.

According to researchers there are two types of hand grips, defined as its purposes the

Pinch and crush grip, where different hand muscles used for gripping purposes where

their number depends or grip use either (needs partial or maximum power grip).

Bookfield (2008) classified grip strength into both Crush and pinch grip as follows: 1)

Crush Grip, It is the same as to grip power, just like handshaking situation where the

hand palm is touched by the four fingers of the hand, this position resembles the strongest

grip for the hand, 2) Pinch Grip, this situation happened in precise griping accurate

situations, when object is held by two or three fingers of the hand (like surgeons and high

tech workers), pinch grip is used to exert and get maximum possible force and 3) Support

Grip, where we use external handle to grip /catch an object, some researchers and

employers used, hand grip strength can be used as strength indicator. According to

17

Boissy et al. (1999), grip strength used as an indicator for overall health and physical

strength. At present, increased employers and organizations use the hand grip tests and

strength as pre-hiring screening measure and as a worker performance indicator (e.g., the

police, the army, fighter pilots, Special Forces and fire departments, etc.). According to

Ruiz-Ruiz et al. (2002) recruiters realized that the hand grip strength is one of the

essential requirements for job applicants that needs physical strengths to pass before

getting their job, like industries included jobs that includes assembly, holding, repairing,

packing, processes, etc. Dubrowski and Carnahan (2004) mentioned that during industry

lifespan the hand grip strength may be used as a labor performance measurement.

According to Bohannon (2004), health experts may use the maximum grip strength as an

upper-limb strength suitable indicator. According to Wind et al. (2009), maximum grip

strength can be used as children and young adults general muscle strength.

1.4 Hand GRIP STRENGTH TEST

There are many muscles used during the power hand grip strength test as follows by

Carlson (1970): 1) The flexor muscles of the arm and 2) The extensors muscles of the

arm. Vanish (2012) stated that both the flexor muscles and the extensors of the arm are

used for grip strength, and to stabilize the wrist, hand grip strength is a result of the hand

ten main muscles as follows: 1) Forearm muscles, 2) Flexor Digitorum Profundus, 3)

Flexor Pollicis Longus, and 4) Flexor Digitorum Superficialis. Finally other muscles

where these muscles that help to make grip according to Gonzalez et al. (1997) such as:

1) Flexor Digitorum, 2) Superficialis, 3) Flexor Carpi Ulnaris, 4) Flexor Carpi Radialis,

and 5) Abductor pollicis. Figure 1-9 show the muscles involved in grip strength

(Vansuh, 2012).

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Figure 1-9 Muscles Involved in Grip Strength (Vansuh, 2012)

1.4 MAXIMUM VOLUNTARY CONTRACTION (MVC)

Segen (2002) defined MVC force as the static measurement of strength which is the

same as the maximum force achieved in one single voluntary effort. According to Tufts’

University Nutrition Collaborative Center (2003), the MVC force is defined, in more

depth, as the power grip force resulted of “forceful flexion of all finger joints” associated

with maximum voluntary force (MVC) that can be achieved under standard bio kinetic

conditions, study revealed by (Brenner et al., 1989; Luna-Heredia et al., 2005) dominant

grip strength increased with age and was greatest for the (35 to 44) year old cohort.

Massy-Westropp et al. (2004) new study performed by Concordia University at the

McGill Nutrition and Performance Laboratory on 203 patients with advanced-stage

cancers finds important relationship between individual’s handgrip strength and cancer

rates survival. The researchers find that simple person handshake (simple squeeze) can

reveal a lot of information about an individual's attitude and character, stated that besides

19

using it as a “diagnostic tool to gauge strength and quality of life among critical patients”

and measure the individuals capability, ability to battle the deadly disease. New research

studies about physical activity effect on middle-age in Boston Medical Center, discussed

through American Academy of Neurology's" annual meeting (2015), found relation

between hand grip strength and walking speed for 2,400 people during 11 years, results

found that “ a slower walking speed in middle age were one-and-a-half times more likely

to develop dementia compared to people with faster walking speed and people with a

stronger hand grip was associated with a 42 percent lower risk of stroke in people over

age 65 This may assist the physicians to determine risk of developing dementia or stroke

for middle-aged people”. According to Sirajudeen et al. (2012), in a study on a total of 50

Indian male population Jamar dynamometer, they found Positive correlation between the

males physical factors like (body mass index, weight, height, anthropometric

measurements) and grip strength. They stated that the grip strength assessment results

considered and accepted as good indicator of “nutritional status, bone mineral content,

muscular strength and functional integrity of upper extremity”, they also have a strong

role to measure treatment strategies results of hand. Mitsionis et al. (2009) conducted a

study using data from the Health and Retirement Survey (HRS), they studied age and

education regressions. They found that “hand-grip strength to produce an easily

interpretable, physical-based measure that allows us to compare characteristic-based ages

across educational subgroups in the United States”, also “a strong handshake can indicate

power, confidence, health, or aggression, the strength of a person’s grip may also be a

useful way to measure true age”. They found that the hand-grip strength testing results be

used as dependable predictor measurement of the human population aging “future

20

mortality, morbidity, cognitive decline and the ability to recover from hospital stays.

Their detailed findings was as follows “The hand-grip strength of 65 year old white

males with less education was the equivalent to that of 69.6 (68.2-70.9) year old white

men with more education, indicating that the more educated men had aged more slowly”.

According to Swift et al. (2012), research objective was to “to assess how age-related

social comparisons, which are likely to arise inadvertently or deliberately during

assessments, may affect older people's performance on tests that are used to assess their

needs and capability”. Using participants from UK centers and senior's lunches in the

South of England, they establish the normal hand grip strength values data and check

relations with the anthropometric factors, by testing 232 participants using the Jamar

dynamometer. They found the following “ Right hand and dominant hand GS were found

to be higher and statistically significant compared to left hand and non-dominant hand

GS, respectively. Men had higher values of GS compared to women, negative association

was observed between age and dominant hand GS, positive association was documented

between height and dominant hand GS, while the respective comparison for weight and

dominant hand GS documented a statistically significant positive association only in the

male group. Positive association between BMI and dominant hand GS was seen in female

individuals. Additional factors associated with GS should be the goal of future

investigations”, as a conclusions they found that “Due to the potential for age

comparisons and negative stereotype activation during assessment of older people, such

assessments may underestimate physical capability by up to 50%, because age

comparisons are endemic, this means that assessment tests may sometimes seriously

underestimate older people's capacity and prognosis, which has implications for the way

21

healthcare professionals treat them in terms of autonomy and dependency”, the key

messages of the Mitsionis et al. (2009) study as follows:

1. “Psychosocial factors may influence how strongly physical effects of ageing manifest

themselves.

2. Age comparison creates a stereotype threat, which can reduce older people's hand

grip strength by up to 50%, Healthcare professionals should be aware of the potential

for age comparison and stereotypes to affect outcomes of assessments of older

people.

3. Hand grip is an ‘objective measure’ of physical capability among older people. It is

predictive of frailty, morbidity, disability and mortality.

4. This research was conducted in a non-medical setting and involved participants in

good health with a small convenience sample. However, the effects remain significant

even when age, gender, education, degree of arthritis in the hands, type of residence

and location of testing.

5. Further research is needed to evaluate the prevalence of age comparisons in clinical

testing settings, and effects on people of different ages.

22

6. Other studies about assessment of muscle status in chronic kidney disease patients

using hand grip strength (HGS) tool and body composition monitor (BCM) in Cairo

University”.

In summary, WMSDs are great in mostly all industries where the job tasks are worked by

hands. Muscle strength is classified by three types according to movement type as

isometric, isotonic, and isokinetic, physical fatigue is can happened for many reasons like

overloads, extended times, abnormal postures and rest periods, researchers muscle fatigue

assessed through three approaches Psychophysical, Physiological, and Biomechanical.

The best approach found in such cases is the psychophysical approach where fatigue is

assessed subjectively by subject individuals, which is used in the current research.

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1.5 PROBLEM STATEMENT

Human muscle fatigue is one of the most researched subjects in ergonomics, improper

human designed job will lead to increased human muscle fatigue that results in high rate

of WMSDs, which incur the industry and organizations a lot of worker compensation

money for their injuries. Many researchers stated that the human muscle fatigue research

are complex in nature, hand grip strength are extremely important factor that have a great

effect on the overhaul human body performance in terms of both the volume of force

exerted and fatigue (endurance limit). Hand grip also can be used as an indicator of

human general health and related to many diseases. Each type of the muscle strength has

its specific use. In Isometric the muscle strength, muscle is used for holding static force.

But in isotonic and isokinetic the muscle strength take place to adjust the dynamic load,

all types of work includes static and dynamic combinations, so this project research will

do both types of forces besides other independent factors like (hand grip circumference,

trade, BMI, holding time, etc.). In calculating the fatigue limits (expressed as

Time/Cycles to Fatigue) for specific aviation, retired and active duty air force and current

technicians/engineers from Jordan, ergonomics researchers used different approaches to

find out the fatigue limits and nature like biomechanical approach in human muscle

fatigue modeling torque and joints stress and or the physiological approach human

muscle fatigue modeling and the rest used psychophysical approach which has a key role

in WMSDs. This research will use the psychophysical approach because of its high

reliability than both biomechanical and physiological approaches, and same decision is

suggested by available literature.

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1.6 RESEARCH OBJECTIVES

A lot of research has been performed to measure the MVC force affected by different

independent factors; however, most of them they resulted in specific (one or two factors

effect) and not a comprehensive model including all parameters to predict the MVC force

either for complete or submaximal of maximum voluntary and fatigue limit t for different

parts of the body (arm, leg, and shoulder). Another issue is that limited literature

available that develops isotonic muscle strength models to predict the fatigue limit, on the

contrary a lot of researchers studied the fatigue limit for isokinetic muscle strength. Also

found that the biomechanical and physiological approaches has less accuracy and lead to

less understanding of fatigue effect. Literature available for last 50 years has different

outputs and point views regarding the effects of many independent variables, besides

using amore precision dynamometer, also many researchers used ANOVA for their

analysis, and very limited used the neural network and fuzzy logic modeling. ANFIS

approach that provides more precise outputs, based on the findings, recommendations

will be made for the applications in appropriate domains. There is a very few researches

about muscle strength, fatigue limits and investigations in the area of hand grip strength

and endurance in aviation trades and especially for those that most of them are smokers

and have an older ages from Jordan. The purpose of the research the primary goal is to

use the psychophysical approach to investigate the hand grip strength MVC and,

endurance fatigue limits in the area of:

A. Aviation trades.

B. Smoker’s aviation trades.

C. Older ages subjects.

25

D. Jordanian Subjects.

E. Include high precision apparatus (Digital dynamometer).

F. Include new factors like forearm, postures, right and left hand.

Investigate and find out the correlations among the different factors of, BMI, hand grip

circumference, resting heart rate, holding time and postures (standing and seating),

submaximal of maximum voluntary contraction or MVC and the number of cycles/time

before the human arm muscle gets fatigued as reported by the aviation individual and his

perception of pain, build models will be built to predict the MVC force which will take

into consideration all independent factors, build prediction fatigue model for my subjects

that involves static force and dynamic force, design another set of models that use both

(isometric and isotonic muscle strengths) to find out the effect of independent variables

on the maximum endurance limit for static force and frequency of gripping for

submaximal isometric muscle fatigue limit (endurance limit). Using all independent

factors, models will be designed accordingly to use the Mathematical and Artificial

neural network and ANFIS fuzzy inference system. Independent Variables and their

levels/notations are shown in Tables 1-1 for the experiment.

Table 1-1 Independent Variables

Dependent Variables Independent

Variables

Treatment Levels

1- MVC

2- Isometric Endurance

Limit (20%, 40%, 60%,

80%)

3- Isotonic Endurance

limit (20-60%)

Age (years) 1) A0: (25-<30)

2) A1: (30-<35)

3) A2: (35-<40)

4) A3: (40-<45)

5) A4: (45-<50)

6) A5: Above 50

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Fixed Factors

1- Jordanian Subjects

2- Digital Dynamometer

Trade 1) APG: Airplane General

2) E&I: Electrical and

Instrument

3) COMNAV: Communication

& Navigation

4) Eng: Engine

5) GSE: Ground Support

Equipment

Smoking 1) Smokers

2) Non-smokers

Body Mass Index

(BMI)

1) Small: S (19-<25)

2) Medium: M (25-<30)

3) Large: L above =>30

Hand Grip

Circumference (CM)

1) Small: S (=< 21.5)

2) Medium: M (>21.5 -23.5)

3) Large: above 23.5

Hand Dominancy 1) D: Dominant

2) ND: Non-Dominant

Forearm

Circumference (CM)

1) Small: S (<= 27.5)

2) Medium: M (>27.5-31)

3) Large: (above 31)

Posture 1) Sitting: SIT

2) Standing: STD

Height (M) 1) Short: S (<= 1.70)

2) Medium: M (>1.70-1.81)

3) Tall: T (above 1.81)

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1.7 RESEARCH SIGNIFICANCE

After the detailed survey of literature, the following observations were made:

1. Limited formal investigation on the effect of the combination of isometric and

isotonic endurance on fatigue has been conducted. Since all workers use the

combination of both of isometric and isotonic forces.

2. Include new factors like (aviation trades, males with more smokers, older ages,

Jordanian subjects, digital dynamometer, standing posture, etc.) besides traditional

researched parameters like gender, BMI, hand grip circumference, resting heart rate,

holding time for isometric forces followed by isotonic contractions and the use of

gloves.

3. Build mathematical models for those independent variables. The current research

will:

A. Assist aviation industry in identifying the influential factors on the human

performance of the jobs that involve the use of hand muscles.

B. Give better understanding about muscle strength in large smoker’s subjects.

C. Give better precision for MVC values by using digital dynamometer.

D. Consider new factors like hand volume and forearm circumference

E. Find relation between different types of sickness and grip strength.

F. Give better idea about race grip strength.

G. Build more precise models like neural network-based and fuzzy logic-based

models.

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1.8 DISSERTATION ORGANIZATION

The present dissertation is a part of a major ongoing research effort to study the different

factors affecting the maximum and partially hand grip strength and belonging factors that

affecting the both isometric and isotonic muscle fatigue, especially in presence of new

parameters that were not studied before. The final goal will be to develop MVC and

fatigue models that can be used to find out the maximum endurance period for isometric

muscle strength and number of cycles for isotonic muscle strength for the all new

parameters. This dissertation is organized into the five chapters as follows: Chapter one

provides a general introduction to human muscles researches their importance and

describes the concepts and meanings of the terms used in the research. Chapter two

introduce very detailed literature review and surveys to explore the information about

(hand grip strength, MVC, isometric muscle strength; isotonic muscle strength, muscle

fatigue, and endurance limit modeling and would be useful for the work. Chapter three

introduce and explain the methodology of the performed experiment including,

instruments and variables used. Chapter four explains and discusses the mathematical and

soft computing models and Chapter five outlines summary of results, their possible

application in aviation industry, and ideas for future research.

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CHAPTER TWO LITERATURE REVIEW

This chapter introduces massive literature on this research in addition to new studies

relevant to the research, the available studies on maximum voluntary control (MVC)

force, isotonic, isometric and isokinetic muscle fatigue limits are discussed, investigated,

reviewed, and classified according to factors which affected the main research. The

significance of the dissertation topic will be elaborated upon and then, in the following

paragraphs, the hand grip strength studies and related subjects will be reviewed.

2.1 MAXIMUM VOLUNTARY CONTRACTION (MVC DEFINITION)

Several authors conducted many research studies to evaluate the effect of different factors

on MVC force. According to Segen (2002) the MVC force is a static human muscle

strength measure and indicates the maximum force that can be achieved in one single

voluntary effort, Kamimura and Ikuta (2001) researched the relation of maximum

isometric contractions and endurance limits, their research resulted in a strength-time

curve relationship between maximum strength and length-time. It had an early peak

followed by gradual decrease in strength.

According to Stulen and De Luca (1981), the maximum voluntary contraction (MVC)

value depends on both of the muscles strength and brain related factors. This is where the

human muscle strength are influenced by different factors like age, skeletal structure,

length and volume of muscles and exercise. According to Stulen and De Luca (1981),

the MVC exerted by two mechanisms, the motor firing frequency and recruitment of that

30

motor, is where the firing frequency initiated by a single motor unit that fires the muscle

fiber. According to De Luca (1985), the relation between the maximum contraction force,

the firing frequency, and number of recruited motor units are directly proportional. Al

Zaman et al. (2007) stated that the smaller motor units are recruited earlier than large

motor units, when human muscle starts to produce force and their firing frequencies start

at higher levels and this is matched by the rule of size principle. Sorensen et al. (2009)

stated that manual tasks workers who used hand static load more frequently, get more

chances in facing muscular disorder complaints, especially the carpel tunnel syndrome,

and he suggested that job design should include the human ergonomics principles,

capabilities and limitations.

Smoking Effect on MVC were researched by many scientists, Asano and Branemark

(1970) mentioned that most of researchers found, and from a medical point view, that

smoking lead to profound vasoconstriction which will develop a microcirculation

complete block that results in tissues starving of nutritive blood and bypass from

arterioles to venues. Isaac and Rand (1969) also mentioned another effect of smoking

where after an average of 30 minutes after smoking, nicotine levels increase in plasma up

to 10 micrograms per 100 mm of blood. Sorensen et al. (2009) mentioned that a smoker

worker’s capabilities decreased because of lung incapacity to provide more oxygen to

muscles. Davis (1960) also mentioned that nonsmokers can exert more force because the

non-smokers cardiovascular system is greatly affected by smoking residue in the body,

where the heart rate (pulse) increased dramatically with each cigarette for an average of

21 beats (pulses) per minute.

31

Hand dominancy effect on MVC were researched by many scientists, Sorensen et al.

(2009) mentioned that a worker’s capabilities are affected by hand dominancy, where

dominant hand can get more MVC. Incel et al. (2002) studied the grip type’s effect (grip

and pinch strength). Their research resulted in favor of the dominant hand. Armstrong

and Oldham (1999) studied the effect of dominancy on hand grip strength between the

non-dominant and dominant hand. For right-handed and left-handed subjects, he found

that that there is important strength difference found in (0.1–3%) in right-handed people

and no worthy difference found for dominancy issues in left-handed people. Ibarra-Majia

et al. (2012) searched the effect of standing and sitting posture on hand and pinch grip

strength. They found that subjects exerted more grip strength for the dominant hand by

3.9%, and for pinch grip, no statistical difference for dominant or non-dominant positions.

Bohannon et al. (2006) searched the left and right hand grip strength, they found that the

dominant right hand is stronger than the monodominant left hand. Koley et al. (2009)

found that the dominant hand had higher grip strength than the non-dominant hand for all

subjects. He introduced the‘‘10% rule’’ and suggested that dominant grip strength is

about 10% greater than the non-dominant grip strength. Petersen et al. (1989) verified

the‘‘10% rule’’ and found that it is applicable only on right hand dominant subjects only.

Left and right hand people effect on MVC were researched by many scientists, Incel et al.

(2002) studied the grip types effect (grip and pinch strength) with left and right hand

people and found that no difference in grip strength between left and right handed

persons. Günther et al. (2008) studied the maximum hand grip strength and found that an

average of hand grip strength was in right: 49 kg; left: 47 kg for males, and right: 29 kg;

left: 27 kg for females. Right hand exerted much more strength than left hand.

32

Armstrong and Oldham (1999) found that there are important strength difference around

(0.1–3%) in right-handed people and no worthy difference found for dominancy issues in

left-handed people. Bohannon et al. (2006) searched the left and right hand grip strength

issue. They found that the right hand is stronger than the left hand. Koley et al. (2009)

found that where he found that sedentary females, equally right and left hand exerts

higher force power than laborers. The study revealed that “dominant grip strength

increased with age and was greatest for the 35 to 44 year old cohort”.

Endurance Limits effect on MVC were researched by many scientists, according to

Miller et al. (1993) endurance is a term that is used to indicate physical fatigue point,

which generally refers to the total time before fatigue state happens. More specifically, it

is according to him "ability to perform prolonged muscular work at predetermined

intensity without external signs of fatigue", Kamimura and Ikuta (2001) conducted an

“evaluation of grip strength with a sustained maximal isometric contraction for 6 and 10

Seconds”, he researched the relation maximum isometric contractions and endurance

limits where they assessed the maximum grip strength. Their research resulted in a

strength-time curve relationship between (maximum strength and strength-time) that has

an early peak followed by gradual decrease in strength.

Different experiment apparatuses used by many scientists, Kamimura and Ikuta (2001)

used the dextral tooling in researching the relation maximum isometric contractions and

endurance limits. Bohannon et al. (2006) searched the left and right hand grip strength

issue on a total of 739 old subjects using the Jamar dynamometer. Kamimura and Ikuta

(2001) researched the relation maximum isometric contractions and endurance limits.

Their test intervals were limited to 6 and 10 seconds.

33

The gender effects on MVC were researched by many scientists, in most researchers the

male can exert more MVC than females, since that the female body and muscles structure

is different from the male body muscles. This result is expected especially when doing

the manual tasks. Sorensen et al. (2009) mentioned that in a worker’s capabilities, males

have more MVC than females. Incel et al. (2002) studied the grip types effect (grip and

pinch strength) on total of 149 male subjects (21 left-handed and 128 right-handed)

volunteers. Günther et al. (2008) studied the maximum hand grip strength, result data as

follows: average of hand grip strength in (right 49 kg; left 47 kg) for males and (right 29

kg; left 27 kg) for females, around 41% lesser than males. Montes (2001) investigated the

muscle volume effect on 38 subjects (24 males and 14 females). Ibarra-Majia et al. (2012)

searched the effect standing and sitting posture effect on both (hand and pinch) grip

strength, he used total of 44 subjects, (30 males and 14 females). Koley et al. (2009)

found the grip strength value for 200 middle aged (18-40) years old female subjects.

34

Table 2-1 Grip Strength Value for 200 Middle Aged Female Subjects.

Right Hand Strength Left Hand Strength

Sedentary (22.75 kg) (23.63 Kg)

Laborers (21.03 Kg) (19.73 Kg)

Miller et al. (1993) used biological approach and by using 6 subjects from males and

females, he searched relationship between muscle characteristics and the strength. He

found that: A) Males are stronger than females, females got 52-66% of male strength and

B) Males are stronger because of muscle fibers size and distribution where females have

less lean tissue in the upper body.

Samples size and aging effects on MVC were researched by many scientists, Kamimura

and Ikuta (2001) research included (50 young subjects of ages 18-26, 25 males and 25

females). Incel et al. (2002) studied the grip types effect for total of 149 subjects (21 left-

handed and 128 right-handed) volunteers. Montes (2001) investigated the muscle volume

effect on grip strength for young subjects (21.87 years) for a total of 38 subjects (24

Males and 14 females). Ibarra-Majia et al. (2012) searched the effect standing and sitting

posture effect on hand and pinch grip strength, on total of 44 subjects, young aged

between (18 to 35) years old (30 males and 14 females). Bohannon et al. (2006) searched

the left and right hand grip strength issue on total of 739 old subjects classified in 4 old

age groups into 75-79, 80-84, 85-89, and 90-99 years. Koley et al. (2009) did a special

study on 200 middle aged (18-40 years) female subjects. Chatterjee and Choudhuri (1991)

found that that highest exerted MVC was for young subjects ages between (18-22 years

old). Petrofsky and Linda (1975) studied the aging effect on males isometric muscle

strength, for endurance limit of 40% of maximal strength and the heart rate and blood

35

pressure used very wide range of ages between (22-60) years old, 100 subjects. Most of

researchers like Asmussen and Heeboll-Nielsen (1955, 1956, and 1962) and Chatterjee

and Chodhuri (1991) obtained results and agreed that max strength can be achieved at

around age 20 as a peak amount then started to decline with older ages. Study revealed

that “dominant grip strength increased with age and was greatest for the 35 to 44 year old

cohort”.

Forearm circumference effects on MVC were researched by many scientists. According

to Fraser et al. (1999), “forearm circumference generally decreased with age for both men

and women, although this decline was less marked for women”. also, “sitting and

standing found that British subjects have slightly greater values for dominant forearm

circumference measurements in both men and women (29.1 cm vs 24.3 cm for men and

25.6 cm Vs 20.4 cm for women)”. Crosby and Wehbe (1994) found that “forearm

circumference delivered the best practical method for assessing the MVC grip strength”,

and muscle mass for both genders. Crosby and Wehbe (1994) showed that using second

handle position of the Jamar dynamometer was adopted for standardized assessment

position produces maximum grip strength measurements for most subjects.

Maximum grip strength effect on MVC were researched by many scientists Kamimura

and Ikuta (2001) obtained strength-time curve relationship between (maximum strength

and strength-time), that has an early peak followed by gradual decrease in strength. When

they researched the relation maximum isometric contractions and endurance limits,

Günther et al. (2008) studied the maximum human hand grip strength, the a average of

hand grip strength (right 49 kg; left 47 kg) for males and (right 29 kg; left 27 kg) for

females, around 41% lesser than males. Bohannon et al. (2006) found that to grip strength

36

inversely proportional with factor aging. Koley et al. (2009) found the following grip

strength values for 200 middle aged (18-40) years old females as shown in Table 2.1.

Chatterjee and Choudhuri (1991) found that the highest exerted MVC was for young

subjects’ ages between (18-22 years old). His study revealed that “dominant grip strength

increased with age and was greatest for the 35 to 44 year old cohort”. Massy-Westropp et

al. (2004) studied the height effect and found high effect as predictive for MVC grip

strength on MVC. Fraser et al. (1999) and Crosby and Wehbe (1994) stated that there is

positive correlation between physical factors and MVC, Grip type (grip and pinch

strength) was studied by many scientists, such as Incel et al., (2002). Their research

resulted in favor of the dominant hand and no difference in grip strength between both

person hands. Ibarra-Majia et al. (2012) searched the effect standing and sitting posture

effect on hand and pinch grip strength. They found that subjects exerted more grip

strength in standing position than sitting position by 3%, and for pinch grip no statistical

difference between standing and sitting positions but key pinch strength marginally

higher for standing and sitting positions, muscle volume. Montes (2001) investigated the

muscle volume effect on grip strength for young subjects (21.87) years for total of, 38

subjects (24 Males and 14 females). He used ultrasonography method to measure the

muscle sectional diameter for both (maximum voluntary isometric contraction position

and relaxation position). Their findings are in the Table 2-1 where higher muscle

diameters noted in the maximum voluntary isometric contraction position. Sherif et al.

(2012) found a positive correlation between higher body physical factors (forearm

anthropometric BMI, and hand muscle) with hand grip strength.

Posture (sitting and standing) effect on MVC were researched by many scientists. Ibarra-

37

Majia et al. (2012) searched the effect standing and sitting posture effect on hand and

pinch grip strength, they found that subjects exerted more grip strength in standing

position than sitting position by 3%, beside that the dominant hand exerted more grip

strength by 3.9% and key pinch strength marginally higher for standing and sitting

positions as shown in Table 2-2.

Table 2-2 Maximum voluntary for Standing and Sitting and Dominant Hand

Test Standing and Sitting Dominant Hand

Grip

Strength

Exerted more grip strength in standing

position than sitting position by 3%

The dominant hand exerted

more grip strength by 3.9%.

Pinch Grip No statistical difference between

standing and sitting positions

No statistical difference for

dominant or non-dominant

positions

Right and

Left hand Key pinch strength marginally higher for standing and sitting positions

Race effect on MVC were researched by many scientists, such as Brenner et al. (1989)

and LunaHeredia et al. (2005) who stated that “the population of South East Scotland

follow previously identified patterns relating to age and sex for other populations”.

Brenner et al. (1989) and LunaHeredia et al. (2005) found that “Spanish population mean

dominant grip strengths of 39.95 kg for men and 25.72 kg for women”. Crosby and

Wehbe (1994) stated that “the United States population in which the mean dominant grip

strength was 137 lb. (62 kg) for men and 81 lb., (37 kg) for women”. Incel et al. (2002)

tested Singaporean population and “get dominant grip strength of 86.06 (24.71) kg” and a

mean non-dominant grip strength of 79.13 (23.68) kg. Koley et al. (2009) did a special

study on 200 middle aged (18-40) years old female subjects of hand grip strength laborers

in India Punjab (Jalandhar). Sherif et al. (2012) performed a study in Indian males and

38

found a positive correlation between higher body physical factors (forearm

anthropometric BMI, and hand muscle) with hand grip strength in Indian males.

Heart rate effect on MVC were researched by many scientists. For example, Wilmore et

al. (2005), Rowell (1993) found that the untrained persons have higher heart rate other

than trained athletes. Sherif et al. (2012) performed a study in Indian males and found a

positive correlation between higher body physical factors (forearm anthropometric BMI,

and hand muscle) and both hand grip strength in Indian subjects. Finally, grip strength

models on MVC were researched by many scientists, Chatterjee and Choudhuri (1991)

searched the grip strength from the following factors (height, weight, age, body surface

areas for both left hands and right hands), and got maximum grip strength regression

model where correlation was positive for all factors and maximum grip strength, beside

that highest exerted MVC was for young subjects ages between (18-22) years old. Table

2-3 shows all experiment results.

39

Table 2-3 MVC Regression Models for MVC

Most of researchers like Asmussen and Heeboll-Nielsen (1955, 1956, and 1962) and

Chatterjee and Chodhuri (1991) identified linear relation for MVC and ages age 20 as a

peak amount then started to decline with older ages as shown in Figure 2-1.

Figure 2-1 Males MVC with Age

40

2.2 ISOMETRIC ENDURANCE LIMIT

Endurance limit has many definitions all of them refer to the human muscle’s capability

and ability of keeping and maintaining a predefined level of force (MVC %) over work

time, thereby making it a force-time relationship. Endurance limit is also defined as force

and time relationship, where the muscles capability and ability to sustain the whole or

percentage amount level of maximum voluntary MVC (Force) over time frame. Yeung

(1998) stated that isometric muscle strength cannot be considered as a good predictor or

indicator of general body health or strength. Mital and Faard, 1990, also has a similar

point of view as Soylu and Arpinar-Aysar (2010) Koley, Kaur and Sandhu (2009) that

isometric muscle strength cannot be considered as a good predictor because of the

absence of body movement and segment throughout maximal voluntary contraction

(MVC).

Endurance time types were classified by researchers, Al Zaman et al. (2007) ergonomics

scientists used the fatigability level limits and physiological characteristics to classify the

motor units into three types as follows: 1) Greatest resistant to fatigue happened in the

type I (S) Motor Units, 2) The average fatigue resistance, the type Ilia (FR) Motor Units

and 3) The weakest (defenseless to fatigue) the type IIb (FF) Motor Units. Biological

Studies were performed by researchers, Yeung and Evans (1998), who made a biological

study on 5 male subject’s fernoris muscle for different isometric voluntary contraction

levels by finding out the relationships of “the vibromyographic (VMG) and the

electromyographic (EMG) signals. A relationship was linear between the frequency

domain (MF) and time domain (RMS). Kaplanis et al. (2009) other biological study

where he measured Biceps Brachii (BB) muscle with 13 different parameters, by their

frequency, time and bispectrum domain, for different isometric voluntary contraction

41

levels (IVC) by calculating the surface electromyography (SEMG) values. He found that

the linear relationship, between the maximum amplitude increases and bispectrum muscle

parameter for all values except the condition of 30% - 50% of maximum IVC. Soylu and

Arpinar-Avsar (2010), searched biologically the fatigue and MVC relationships on 12

subjects biceps brachii muscles (BBM), by using the surface electromyography (SEMG)

signals. He found that minor increase in the force with biceps brachii muscles (BBM) can

reach the maximum (MVC) within two seconds only.

Sample subjects and aging effect on isometric endurance limit studied by researchers,

Yeung and Evans (1998) made a biological study on 5 male subjects’ fernoris muscle.

Garg et al. (2002) studied the relationship between endurance limits and different MVC%

from elbow flexion angles for 12 females. Soylu and Arpinar-Avsar (2010) searched

biologically the fatigue and MVC relationships on 12 subjects. Chatterjee and Chowdhuri

(1991) searched the MVC force, 40% of MVC, all age groups, and right hand

(dominancy) relationship. He found that no relationship between endurance values limit

and age. Chatterjee and Chowdhuri (1991) searched the fraction MVC at 40% level

where they found: 1) 40% MVC level independent of gender and age and 2) Dominant

hand sustained extra endurance limit average of 16 seconds more than the non- dominant

hand. Miller et al. (1993) used biological approach and by using 16 subjects from males

and females, he researched a relationship between muscle characteristics and the strength.

He found that males are stronger than females. Females got 52-66% of male strength. In

addition, males are stronger because of muscle fibers size and distribution and females

have less lean tissue in the upper body. Endurance and postures effect on isometric

endurance limit studied by researchers, Mogk and Keir (2003) measured the forearm

42

fatigue response for forearm posture and wrist combinations as shown in Table 2-4:

Table 2-4 MVC Fractions with Wrist Postures Effect

MVC Fractions Wrist Postures Three Forearm Postures

(5%, 50%, 70%, 80%,

and 100%)

(neutral, flexed, and

extended)

(pronated, neutral, and

supinated)

Results were as follows: 1) Wrist postures flexed affected grip force with different

forearm posture, 2 other wrist postures got altered muscle contributions and 3) Wrist

postures flexion got the highest muscle activation. Haque and Khan (2009) researched the

relationship between maximum voluntary contractions with different postures (ulnar

deviation of the wrist), he found that: 1) Wrist neutral position got the highest MVC, 2)

The MVC values getting higher when lower ulnar deviation and increase ulnar deviation

and 3) O’Driscoll et al. (1992) got different result where it was mentioned that the self-

selected position resulted in getting the highest MVC. Haque and Khan (2009, searched

the wrist posture effect and found that the best posture and most comfortable is that

where the wrist posture was closer to the neutral position. Figure 2-2 show various hand

wrist posture

43

Figure 2-2 Various Hand Wrist Postures Used (Khan, 2010)

Endurance relationships and models found by researchers, Yeung and Evans (1998) made

a biological study on 5 male subjects’ fernoris muscle for different isometric voluntary

contraction levels by finding out the relationships of “the vibromyographic (VMG) and

the electromyographic (EMG) signals. He found that the relationship was linear between

the frequency domain (MF) and time domain (RMS). Kaplanis et al. (2009) did another

biological study where he measured Biceps Brachii (BB) muscle with 13 different

parameters: their frequency, time and bi spectrum domain, for different isometric

voluntary contraction levels. By calculating the surface electromyography (SEMG)

values, he found that linear relationship exists between the maximum amplitude increases

and bi spectrum muscle parameter for all values except the (30-50%) envelop of

maximum MVC. Garg et al. (2002) searched the fraction of MVC and endurance level

for different (elbow flexion angles), he found that from (15% to 30%) MVC, the

endurance limit decreasing in high rate and from (30% - 90%) MVC the endurance limit

decreasing in slower rate increased rapidly, as result there is Nonlinear relationship

between MVC force and time, Minnal (2014) found that the curve is never asymptotic

and inversely proportional between MVC and endurance limit for certain elbow flexion

angle (increase the fraction MVC resulted in reduce endurance limit). Al Meanazel

(2013) found out that the maximum endurance limit results for nonsmoker male that have

both higher BMI and higher grip circumference when using the dominant hand.

Endurance level at different MVC% effect on isometric endurance limit studied by

researchers, Garg et al. (2002) studied the relationship between endurance limits and

44

different MVC% from elbow flexion angles for 12 females. Results were as follows, as

shown in Figure 2-3 (Garg et al., 2002):

1. In general, inversely proportional continuous non-linear relationship between (a

decrease endurance limits during increase MVC %).

2. Up to (30%) of MVC%, High rate or decline in endurance limit with even increase in

MVC%.

3. From (30% to 90%) of MVC%, slower rate decline in endurance limit with even

increase in MVC%.

4. At (5%) of MVC, “curve does not become asymptotic” even at 5% of MVC and

different MVC percentages.

Figure 2-3 Endurance Limit Vs MVC% (Different Shoulder Posture)

45

Soylu and Arpinar-Avsar (2010) searched biologically the fatigue and MVC relationships

on 12 subjects biceps brachii muscles (BBM), by using the surface electromyography

(SEMG) signals. He found that minor increase in the force with biceps brachii muscles

(BBM) can reach the maximum force level (MVC) within two seconds only. Rohmert

(1960) searched and proposed an endurance limit model for partial MVC (15% of MVC)

on the following assumptions where he assumed that subject can sustain 10-15 minutes

static contraction sustained without tiring. The relation between MVC% and endurance

limit where higher of MVC% which lead to decreased endurance limit as shown in Figure

2-4.

Figure 2-4 Endurance Limit for Different % of MVC, Rohmert (1960)

Nag (1991), Rose et al. (1992), Sjogaard et al. (2000), and Garg et al. (2002) reviewed

Rohmert’s curve and stated that Rohmert’s curve was misjudging (i.e., overestimating)

46

the endurance limit duration at MVC low percentages. Bjorksten and Jonsson (1977)

researchers got similar results to Rohmert curve above between MVC holding force and

endurance time. Chatterjee and Chowdhuri (1991) searched the MVC force, (40%) of

MVC, in all age groups, and right hand (dominancy) relationship, he found that no

relationship between endurance values limit and MVC. Rohmert (1968) searched the

endurance limit and gender relationship, he stated that: 1) No important difference found

between females and males isometric muscle strength endurance time and 2) Endurance

limit is independent of the subject sex with requirement that they work at same MVC.

Eksioglu (2011) researched the static force endurance limits including more variables

(anthropometric variables, BMI, grip span) in a trial to get a comprehensive model and

compared it with the other research models. He got the following results at 30% MVC.

Other research models got altered values (lower or higher) and found similar results for

other percentages as shown in Figure 2-5 by Eksioglu (20

Figure 2-5 Endurance Limit Of 40% of MVC of Left Hand and Right Hands of 93

Men (Chatterjee & Chowdhuri, 1991)

47

Garg et al. (2002) searched the fraction of MVC and endurance level for different (elbow

flexion angles) and revealed the following:

A. Nonlinear relationship between MVC force and time according to Minnal,

2014 (The curve is never asymptotic).

B. Inversely proportional between MVC and Endurance limit for certain elbow

flexion angle (increase the Fraction MVC resulted in Reduce Endurance Limit).

C. From 15% to 30% MVC, the endurance limit decreasing in high rate.

D. 30% -90% MVC the endurance limit decreasing in slower rate increased

rapidly.

Chatterjee and Chowdhuri (1991) searched the fraction MVC at 40% level where they

found: 1) 40% MVC level independent of gender and age and 2) Dominant hand

sustained extra endurance limit average of 16 seconds more than the non-dominant hand.

Mogk and Keir (2003) measured the forearm fatigue response for forearm posture and

wrist combinations for different fractions of MVC (5%, 50%, 70%, 80%, and 100%)

with three forearm postures (neutral, pronated, and supinated) and different wrist postures

(extended, flexed, and neutral) and, results were as follows: 1) Wrist postures flexed

affected grip force with different forearm posture, 2) Other wrist postures got altered

muscle contributions and 3) Wrist postures flexion got the highest muscle activation. Al

Meanazel (2013) used the psychophysical approach on 120 subjects (males and females)

to get a model for endurance limit (number of cycles to fatigue). Considering the

following independent factors: BMI, hand grip circumference gender, hand dominancy

and mode of contraction, he found out that the maximum endurance limit results for

nonsmoker male that have both, higher BMI and higher grip circumference, when using

the dominant hand.

Endurance fatigue and MVC effect on isometric endurance limit studied by several

48

researchers (Rohmert, 1960a, 1960b; Merton, 1654; Funderburk et al., 1974) stated that

when muscle contraction tension go beyond the (10-15% envelope) maximum voluntary

strength of the human muscle’s, the muscle fatigue rate increases rapidly. Chatterjee and

Chowdhuri (1991) researched the MVC force. 40% of MVC in all age groups, and right

hand (dominancy) relationship, he found no relationship between endurance values limit

and MVC. Al Meanazel (2013) found out that the maximum endurance limit results for

nonsmoker male that have both (higher BMI and higher grip circumference) when using

the dominant hand. Rohmert (1960a; 1960b), Merton (1654), and Funderburk et al. (1974)

stated that in case of that isometric exercise, the muscle fatigue rate increased rapidly

specially when muscle contraction tension go beyond the (10%-15% envelope) of the

maximum voluntary strength of the human’s muscles. Rohmert (1968) researched the

endurance limit and gender relationship. He stated that no important difference was found

between females and males isometric muscle strength. Endurance time and endurance

limit is independent of the subject’s sex, with requirement that they work at same MVC.

Kumar et al. (1991), Mital and Genaidy (1989), Mital et al. (1986) stated that isometric

muscle strength cannot be considered as a good predictor or indicator of general body

health or strength. Mital and Faard (1990) also have similar points of view as Kumar et al.

(1991) and Mital and Genaidy (1989). Mital et al. (1986) that isometric muscle strength

cannot be considered as a good predictor because of the absence of body movement and

segment throughout the maximal MVC. Funderburk et al. (1974) mentioned that for

isometric strength at 15% of MVC, holding time did not vary between high and low BMI

persons.

Fatigues differences between individuals (stronger and weaker) effect on isometric

49

endurance limit studied by researchers, Caldwell (1964) and Start and Graham (1964)

researched the effect of fatigue on both stronger and weaker individuals. He found that

the different levels of grip strength holding time don’t vary between weaker and stronger

individuals when subjected to the same load and this might happen because loads used

were small loads, for Kroll (1968) Mundale (1970) the story was different when both

weaker and stronger persons subjected to (medium to high) MVC levels. The weaker

person maintained better endurance in activities especially at high MVC%. Miller et al.

(1993) researched the relationship between muscle characteristics and the strength, he

found that: 1) Males are stronger than females, females got 52-66 % of male strength and

2) Males are stronger because of muscle fibers size and distribution where females have

less lean tissue in the upper body. Fatigue between left and right (dominancy) effect on

isometric endurance limit studied by researchers, Chatterjee and Chowdhuri (1991)

searched the MVC force, 40% of MVC, all age groups, and right hand (dominancy)

relationship. The research resulted in the following: 1) The right hand contraction

endurance limit found meaningfully greater than left hand contraction endurance limit,

and 2) Right and left hand contraction endurance limit different by 16 seconds. Chatterjee

and Chowdhuri (1991) researched the fraction MVC at 40% level where they found that

the dominant hand sustained extra endurance limit an average of 16 seconds more than

the non-dominant hand. Al Meanazel (2013) found out that the maximum endurance limit

results when using the dominant hand.

Endurance and (height, weight, BMI and body surface area) effect on isometric

endurance limit studied by researchers, Chatterjee and Chowdhuri (1991) searched the

MVC force, 40% of MVC, of all age groups, and right hand (dominancy) relationship. He

50

found that no relationship between endurance values were limited by height, weight, and

body surface area, as per the results shown in Figure 2-6. Chatterjee and Chowdhuri

(1991) and Funderburk et al. (1974) mentioned that for isometric strength at 15% of

MVC, holding time did not very between high and low BMI persons. Dennerlein et al.

(2002) searched the forearm fatigue and repetitive task relationship using the

psychophysical approach. Al Meanazel (2013) found out that the maximum endurance

limit results for nonsmoker male that have both higher BMI and higher grip

circumference when using the dominant hand. Sherif et al. (2012) performed a study in

Indian males and found a positive correlation between higher body physical factors

(forearm anthropometric BMI, and hand muscle) with hand grip strength in Indian males.

Endurance limit and gender effect on isometric endurance limit studied by researchers,

Rohmert (1968) searched the endurance limit and gender relationship, he stated that: 1)

No important difference found between females and males isometric muscle strength

endurance time and 2) Endurance limit is independent of the subject sex with requirement

that they work at same MVC

Carlson (1969) and Miller et al. (1993) found that males withstand greater absolute forces

than females at target force. Hunter and Enoka (2001) researched the relative reductions

in MVC force at exhaustion and found no difference between males and females where

they had parallel reductions in Maximum voluntary contraction at MVC force at

exhausting state. Chatterjee and Chowdhuri (1991) researched the fraction MVC at 40%

level where they found 40% MVC level independent of gender and age. Al Meanazel

(2013) found out that the maximum endurance limit results for nonsmoker males that

have both (higher BMI and higher grip circumference) when using the dominant hand.

51

Miller et al. (1993) researched the relationship between muscle characteristics and the

strength. He found that males are stronger than females. Females got 52-66% of male

strength and males are stronger because of muscle fibers size and distribution where

females have less lean tissue in the upper body. Static force endurance limit effect on

isometric endurance limit studied by researchers, Eksioglu (2011) researched the static

force endurance limits including more variables (anthropometric variables, BMI, grip

span) in atrial to get a comprehensive model and compared it with the other researcher’s

models. He got the following results: at 30% MVC other researcher’s models get altered

values (lower or higher) and similar for all other percentages. Chatterjee and Chowdhuri

(1991) researched the fraction MVC at 40% level where they found 40% MVC level both

independent of the age and or gender.

Figure 2-6 Endurance Limits Of 40% of MVC of Left Hand and Right Hands

52

Endurance and gender influence on isometric endurance limit studied by researchers

Chatterjee and Chowdhuri (1991) researched the fraction MVC at 40% level where they

found 40% MVC level independent of gender and age. And finally endurance approaches

and different devises used in researches, Dennerlein et al. (2002) searched the forearm

fatigue and repetitive task relationship using the psychophysical approach. Mogk and

Keir (2003) measured the forearm fatigue response for forearm posture and wrist

combinations using the EMG (Electromyography). Al Meanazel (2013) used the

psychophysical approach on 120 subjects (males and females) to get a model for

endurance limit (number of cycles to fatigue). Dennerlein et al. (2002) found that

repetitive tasks have a great effect on the forearm fatigue.

2.3 ISOTONIC MUSCLE FATIGUE

Isotonic muscle fatigue deals with the exerted amount of force and speed limits. The IMF

researches were limited and inadequate in the biomechanical and physiological

approaches mostly used to build the isotonic muscle fatigue. The first IMF researches

conducted in early 19th

century were Fenn and Marsh (1935) on cat and frog subjects.

They researched the force application and velocity relationship and got an exponential

curve relationship. Garg and Beller (1994) found that speed is a very important factor for

isotonic muscle strength. Al Meanazel (2013) used the psychophysical approach on 120

subjects (males and females) to get a model for endurance limit (number of cycles to

fatigue). Considering the following independent factors (BMI, hand grip circumference

gender, hand dominancy and mode of contraction), he found out that the maximum

endurance limit results for nonsmoker male that have both (higher BMI and higher grip

circumference) when using the dominant hand.

54

2.4 ISOKINETIC MUSCLE FATIGUE

Garg and Beller (1994) were among first researchers for isokinetic and isotonic muscle

strength, they got the following results: 1) In isotonic muscle strength, the speed has a

major rule, 2) Conflict between isokinetic lifting capability and individual subjective

judgment of physical stresses where appeared in determining the speed of lifting and 3)

The high speed lifting more comfortable than slow speed lifting which is contrary to

ergonomics principles

Yessierli et al. (2009) researched the isokinetic muscle strength for 24 subjects that have

two major groups: young (18-25 years) and old (55-65 years). They wanted to simulate

common material handling tasks and they looked for a model that included the following

parameters: gender, age, and task data, for extended isokinetic repetitive intermittent

torso exercises until exhaustion. Yessierli et al. (2009) used the electromyographic

(EMG) signals, MVC readings, and subject perceived discomfort to evaluate fatigue

progression. He used fraction MVC (30 to 40% of MVC) and (30-60) seconds duty cycle.

They got the following results

A. Younger group have 23% more MVC than older group.

B. Marginal fatigue effect on gender and age.

C. Significant interactive fatigue effects of both gender and age on effort level.

Where older ages group should have tasks with lower effort levels.

D. Endurance time will be reduced by 30% for loads increased by 10%.

Garg et al. (1994) used the psychophysical approach by using the bio kinetic ergometer

with load cell and low back subjective perception. They used 9 male subjects in their

55

experiment to find out the relationship between isokinetic lifting strengths and the speed

of lifting. They found the following results:

A. Inversely proportional between the isokinetic lifting strengths (Peak and mean)

with lifting speed.

B. Inversely proportional between the isokinetic lifting strengths (Peak and mean)

with box width.

C. Box width has less effect than Lifting speed.

D. High speed lifting more comfortable than low speed lifting (subjects

judgment).

E. Maximum allowable load is equal to high speed lifting and according to above

results, they recommended that job task designed for best lifting speed and box

widths combination to get the optimal isokinetic lifting strength and least workers

complaints.

F. The larger hand grip circumference can be contributed to a mechanical

advantage.

56

2.5 GRIP STRENGTH NEW RESEARCH AREAS

According to Kilgour et al., (2010), a new study performed by Concordia University at

the McGill Nutrition and Performance Laboratory on 203 patients with advanced-stage

cancers, finds an important relationship between individuals handgrip strength and

cancer rate survival. The researchers find that simple person handshake (simple squeeze)

can tell and reveal a lot of information about an individual's attitude and character and

stated that beside using it as a “diagnostic tool to gauge strength and quality of life

among critical patients”, it can measure the individuals capability and ability to battle the

deadly disease. A study was performed by letting the advanced-stage cancer patients use

their dominant hand to squeeze a dynamometer and measure the maximum exerted MVC

(the patients peak grip strength). According to researcher, Kilgour et al., (2010), "this

measure is one of several to categorize patients according to the severity of their disease.

It can help determine interventions they may need, whether clinical, nutritional or

functional". Kilgour et al. (2010) also stated that the study may result in that the

handgrip test could be used as a “better alternative” to measure and find out the

participants body strength and their decline rate than other traditional used ways like

decreasing body weight. The Handgrip strength MVC test will predict the patients

survival rates “associated with changes in body composition, nutritional status,

inflammation, and functional ability in several chronic disease conditions". This test will

guide cancer patients to enhance “their physical and mental health by engaging in

physical activity and eating healthier”. According to a study done by Sirajudeen et al.

(2012) they found positive correlation between the males physical factors like body mass

index, weight, height, beside hand anthropometric measurements, and grip strength. By

using this, they stated that the grip strength assessment results were considered and

http://www.topendsports.com/testing/tests/handgrip.htm

57

accepted as good indicators of “nutritional status, bone mineral content, muscular

strength and functional integrity of upper extremity”. They also have a strong role to

measure treatment strategy results of hand.

According to a study performed by Sanderson WC, Scherbov (2014), they used data from

the Health and Retirement Survey (HRS) from two groups, less and higher than high

school diploma level, and “studied regressions on hand-grip strength that were run for

each sex and race using age and education. Their interactions and other covariates, as

independent variables”, they found that “hand-grip strength produces an easily

interpreTable, physical-based measure that allows us to compare characteristic-based

ages across educational subgroups in the United States”. They also found “a strong

handshake can indicate power, confidence, health, or aggression”. “The strength of a

person’s grip may also be a useful way to measure true age”. They found that the hand-

grip strength testing results be used as a dependable predictor measurement of the human

population aging “future mortality, morbidity, cognitive decline and the ability to

recover from hospital stays. Detailed findings as follows: “the hand-grip strength of 65

year old white males with less education was the equivalent to that of 69.6 (68.2, 70.9),

year old white men with more education, indicating that the more educated men had aged

more slowly”. According to Garg (1994), new research studies about physical activity

effect on middle-age in Boston Medical Center, discussed through American Academy

of Neurology's" annual meeting (2015) find relation between hand grip strength and

walking speed for 2,400 people during 11 years. Results found that “a slower walking

speed in middle age people were one-and-a-half times more likely to develop dementia

compared to people with faster walking speed and people with a stronger hand grip was

58

associated with a 42 percent lower risk of stroke in people over age 65. “ This may assist

the physicians to determine risk of developing dementia or stroke for middle-aged

peoples.”

According to Swift et al. (2012) in this researchers objective was to “to assess how age-

related social comparisons, which are likely to arise inadvertently or deliberately during

assessments, may affect older people's performance on tests that are used to assess their

needs and capability”. They used participants from UK centres and senior's lunches in the

south of England. They established the normal hand grip strength values data and

checked relations with the anthropometric factors by testing 132 participants using the

jamar dynamometer. They found the following: “ right hand and dominant hand gs were

found to be higher and statistically significant compared to left hand and nondominant

hand gs. Respectively, men had higher values of gs compared to women. A negative

association was observed between age and dominant hand gs, and a positive association

was documented between height and dominant hand gs; while the respective comparison

for weight and dominant hand gs documented a statistically significant positive

association only in the male group. A positive association between bmi and dominant

hand gs was seen in female individuals. “Additional factors associated with gs should be

the goal of future investigations.” As a conclusion, they found that “due to the potential

for age comparisons and negative stereotype activation during assessment of older people,

such assessments may underestimate physical capability by up to (50%), because age

comparisons are endemic. This means that assessment tests may sometimes seriously

underestimate older people's capacity and prognosis, which has implications for the way

59

healthcare professionals treat them in terms of autonomy and dependency”. According to

Swift et al., (2012), the key messages of the study are as follows: “

1. Psychosocial factors may influence how strongly physical effects of ageing

manifest themselves.

2. Age comparison creates a stereotype threat, which can reduce older people's

hand grip strength by up to (50%), as large as the normal range from middle to

old age.

3. Healthcare professionals should be aware of the potential for age comparison

and stereotypes to affect outcomes of assessments of older people.

4. Hand grip is an ‘objective measure’ of physical capability among older

people. It is predictive of frailty, morbidity, disability and mortality.

5. This first experimental test of the impact of age comparison on older people's

hand grip strength demonstrates that it is impaired by comparison with younger

people.

6. This research was conducted in a non-medical setting and involved

participants in good health with a small convenience sample. However the effects

remain significant even when age, gender, education, degree of arthritis in the

hands, type of residence and location of testing are accounted for.

7. Further research is needed to evaluate the prevalence of age comparisons in

clinical testing settings, and effects on people of different ages.

60

8. Other studies about Assessment of Muscle Status In Chronic Kidney Disease

Patients Using Hand Grip Strength (HGS) Tool And Body Composition Monitor

( BCM) in Cario University” .

61

CHAPTER THREE RESEARCH METHODOLOGY

3.1 INTRODUCTION

This chapter explains the methodology and procedures used to measure and evaluate the

maximum voluntary contraction, and the fatigue and endurance limits (number of cycles

and time until fatigue) for both isometric and isotonic strength of subjects from the

profession of aviation mechanics. Before the actual experiment officially started, a pilot

study was conducted to qualify the experimental independent and dependent variables,

and to evaluate the apparatus used and experimental procedure.

3.2 EXPERIMENTAL ELEMENTS

The subjects were 132 retired and active-duty mechanics from the Royal Jordanian Air

Force. All of them participated in each of the three tests (maximum voluntary contraction

(MVC), isometric muscle fatigue test, and isotonic muscle fatigue test). Subjects were

healthy males who did not have any physical injuries related to the hand. Anthropometric

measurements have been collected. Descriptive statistics are shown in Table 3-1 for

subjects in the three experiments.

Table 3-1 Descriptive Statistics of Aviation Male Subjects

Variable Mean Standard Dev Minimum Maximum

Age (Y) 41.712 7.833 25.000 65.000

Weight (Kg) 82.600 12.850 55.000 114.00

Height (M) 1.7581 0.0705 1.5500 1.9300

BMI 26.679 3.600 18.711 37.422

HGC (CM) 22.523 1.338 19.500 25.500

FAC (CM) 29.341 2.441 23.000 35.000

62

The dependent and independent variables are shown in Table 3-2 with their levels. They

included most of researched variables during the last sixty years and included many new

variables as well.

Table 3-2 Dependent, Independent Variables and Treatment Levels

Dependent Variable Independent

Variable Treatment Level

1- MVC


Limit (20%, 40%, 60%

and 80%)

3- Isotonic Endurance Limit

(20-60%)

Fixed Factors



Age (years)

1) A0: (25 – 30)

2) A1: (30 – 35)

3) A2: (35 – 40)

4) A3: (40 – 45)

5) A4: (45 – 50)

6) A5: (> 50)

Trade

1) APG: (Airplane General)

2) E&I: (Electrical and Instrument)

3) COMNAV: (Command & Navigation)

4) Eng: (Engine)

5) GSE: (Ground Support Equipment)

Smoking 1) Smokers

2) Nonsmokers

Body Mass

Index

(BMI)

1) Small: S (19 – 25)

2) Medium: M (25 – 30)

3) Large: L (> 30)

Hand Grip

Circumferen

ce (CM)

1) Small: S (<= 21.5)

2) Medium :M (21.5 – 23.5)

3) Large: (> 23.5)

Hand

Dominancy

1) D: Dominant

2) ND: Non Dominant

Forearm

Circumferen

ce (CM)

1) Small: S (<= 27.5)

2) Medium: M (27.5 – 31)

3) Large: (> 31)

Posture 1) Sitting : SIT (Sitting)

2) Standing: STD (Standing)


2) Medium: M (1.70 – 1.81)

3) Tall: T (> 1.81)

63

The apparatus used in this experiment was digital Camry Hand grip Dynamometer

(Figure 3-1) to measure both the Maximum Voluntary Contraction (MVC in Kgs) and

endurance limit (test time to fatigue, Seconds). This apparatus has an adjustable grip to

suit subjects hand circumferences. Gollehon extendable Goniometer (Figure 3-1) was

used to assess and set the subjects’ joint angles at 90 degrees for elbows, knees, and hip

in the sitting position. A measuring tape was also used to measure the height and the hand

GC. A digital stop watch was used to record the endurance limit (the time was recorded

to the nearest second). Finally, a digital scale was used to measure weights (rounded to

the nearest 0.1 Kgs).

Figure 3-1 Experimental Instruments

The overall research methodology is shown in Table 3-3, which describes the procedures

to conduct the maximum voluntary contraction test (MVC) (Kgs), and isometric muscle

fatigue limit test (time to fatigue; Seconds), and isotonic muscle fatigue test (time to

fatigue; Seconds).

64

Table 3-3 Overall Research Methodology for Aviation Subjects

Start

Participants

132 Air force technicians (Retired/Active, Male, Jordanian)

Gather Anthropometric Data (Independent Variables)

1. Age 2. Weight

3. Hand grip circumference (GC) 4. Smoking conditions

5. Dominant hand 6. Height

7. Trade 8. Forearm Circumference

9. Calculated BMI 10. Dominancy

11. Forearm circumference

Maximum Grip Strength Test

1. Seated with 90 joint angles (hip, knees, and elbows)

2. Hand grip Dynamometer adjusted to fit the GC

3. Each subject to exert maximum force on the Dynamometer

4. Do three Maximum Grip strength tests (for MVC) for 5 seconds each with

dominant hand, and 5-minute rest.

5. Repeat Steps 1-5 for the non-dominant hand

Isometric Endurance Limit Test

1. Seated with 90 joint angles (hip, knees, and elbows)


3. Hold at 20%, 40%, 60% and 80% of MVC until fatigue (e.g., 25%, 50%, 75%)

4. Rest for 5 minutes

5. Have heart rate recorded


Isotonic Endurance Limit Test

1. Seated with 90 joint angles (hip, knee, and elbow)


3. Move pointer Hand grip Dynamometer between 20% and 60% as fast as

possible until fatigue.

4. Rest for 5 minutes


Repeat The Above Three Tests for Standing Posture

65

3.3 EXPERIMENTAL PROCEDURE

The objective of the research is to find and verify the major factors that affect static and

dynamic grip forces in exertion and obtain the measurements for (1) MVC; (2) isometric

muscle fatigue limit test (20%, 40%, 60% and 80%) for the time to fatigue (Seconds);

and (3) isotonic muscle fatigue test (20-60%) for the time to fatigue (Seconds). Subjects

participate in the experiment at the same time and under the same conditions. The

following paragraph explains the detailed steps of the experiment. Figure 3-2 shows the

subject’s posture during the experiment.

Figure 3-2 Subject Posture during the Tests

The MVC experiment was performed on all the 132 participants as follows:

1. Measure height and weight by using a digital scale and a measuring tape. Each

aviation subject was asked to sit on an adjustable chair and several volunteers were asked

to make sure that their joints (hip, knees, and elbows) maintain 90˚ angel by using the

Gollehon extendable goniometer. Also, they were asked to keep their feet on the floor.

Then, the subject’s hand HGC and FAC radius were measured using the dynamometer

fixed scale where subjects should match grip size and then do the following:

66

A. Determine the initial maximum voluntary contraction (MVC) force when the

subject is at rest and in a neutral posture.

B. MVC was measured by telling the subject to hold the hand grip dynamometer

using one hand with the dynamometer scale hidden from the subject (to measure

the voluntary contraction without over exertion).

C. Each subject performed three MVCs for 5 seconds for each trial and with 5-

minute resting period.

D. Above procedure repeated for each hand to check dominancy effect.

E. Record the maximum peak MVC for each subject.

F. Repeat above procedure for standing position.

In the isometric muscle fatigue test, the aim was to measure the endurance limit for

isometric muscle strength at the following percentages of MVC: 20%, 40%, 60%, and

80%. The experiment was similar to the MVC test as follows:

A. Record subject’s height and weight by using a digital scale and a measuring tape.

B. Each subject was asked to sit on an adjustable chair, and volunteers were asked to

make sure that their joints (hip, knees, and elbows) are at 90˚ by using the Gollehon

extendable goniometer. Subjects were asked to keep their feet on the floor.

C. Measure the subject’s hand GC and FAC radius with dynamometer fixed scale

where each subject should match grip size.

D. Subject was asked to hold the hand grip dynamometer with one hand at a time at

each designed percentage (20%, 40%, 60% and 80%), and was asked keep holding

each partial MVC until pain and feeling of fatigue starts in their arm.

E. Record the time in seconds to fatigue for each partial MVC.

F. Give subject a 5-minute break.

G. Repeat above procedure for standing position.

In the isotonic muscle fatigue test, the aim was to measure the endurance limit for

isotonic muscle strength at the following percentages of the MVC: 20-60%. The

experiment was similar to the MVC test as follows:

67

A. Record subject’s height and weight by using a digital scale and a measuring tape.

B. Each subject was asked to sit on an adjustable chair, and to make sure that their

joints (hip, knees, and elbows) are at 90˚ by using the Gollehon extendable

goniometer. Subjects were asked to keep their feet on the floor.

C. Measure the subject’s hand GC and FAC radius on dynamometer fixed scale

where each subject should match grip size.

D. Subjects were asked to move the moving scale on the hand grip dynamometer

continuously without stopping at any force between 20% and 60% of MVC until

they start feeling pain and fatigue in their arm in two phases.

E. Fast mode (as fast as possible between 20% and 60%).

F. Slow mode (at normal speed between 20% and 60%).

G. Record the number of cycles to fatigue for each partial MVC which are used as an

indication of isotonic muscle fatigue limit.

H. Give subject a 5-minute break.

I. Repeat for hand dominancy

J. Repeat above procedure for standing position

68

3.4 DATA ANALYSIS AND MODELING

The analyses conducted in this research are listed in Table 3-4. The analysis included an

analysis of variance (ANOVA) (using Minitab 17) followed by the use of different

modeling techniques to build models (to predict MVC, isometric endurance limit, and

isotonic fatigue limit) using Minitab 17 and Matlab 15.

Table 3-4 Data Analysis and Modeling Methodology

MVC data Isometric Muscle Fatigue

(Endurance Limit Data)

Isotonic Muscle Fatigue

(Endurance Limit Data)

Descriptive statistics (Model Adequacy Checks)

Perform MANOVA

for dependent variables (MVC, Endurance limit and no. of cycles to fatigue) and for

independent variables

Perform ANOVA

for dependent variables (MVC, Endurance limit and no. of cycles to fatigue) and for

independent variables

Develop Linear Regression (LR) Models

Develop Non-Linear Regression (NLR) Models

Develop Neural Network Model

Develop Adaptive Neuro Sugeno Fuzzy Inference System (ANFIS) Model

69

CHAPTER FOUR RESULTS AND DISCUSSION

4.1 INTRODUCTION

As previously mentioned, the experiments were performed on 132 male subjects (20 to

60 years old) that represent retired and active-duty engineers and technicians from the

Royal Jordanian Air Force. The data was presented, analyzed and discussed in this

chapter. As previously mentioned, the dependent variables are as follows:

1. Maximum voluntary contraction (MVC) test: MVC (in Kgs).

2. Isometric muscle fatigue limits test: endurance limits (in Seconds) for different

MVC ratios (20%, 40%, 60% and 80%).

3. Isotonic muscle fatigue test: endurance limits (in Seconds) at both high and low

speeds.

The dependent variables and independent factors are shown in Table 4-1.

Table 4-1 Dependent and Independent Variables with their Levels


Variables

Treatment Levels

4- MVC


Limit (20%, 40%, 60%,

80%)


limit (20-60%)

Fixed Factors



Age (years) 1. A0: (25-<30)

2. A1: (30-<35)

3. A2: (35-<40)

4. A3: (40-<45)

5. A4: (45-<50)

6. A5: Above 50

Trade 1. APG: Airplane General

2. E&I: Electrical and

Instrument

3. COMNAV: Communication

& Navigation

4. Eng: Engine

5. GSE: Ground Support

Equipment

70

Smoking 1. Smokers

2. Non-smokers

Body Mass Index

(BMI)

1. Small: S (19-<25)

2. Medium: M (25-<30)

3. Large: L above =>30

Hand Grip

Circumference (CM)

1. Small: S (=< 21.5)

2. Medium: M (>21.5 -23.5)

3. Large: above 23.5

Hand Dominancy 1. D: Dominant

2. ND: Non-Dominant

Forearm

Circumference (CM)

1. Small: S (<= 27.5)

2. Medium: M (>27.5-31)

3. Large: L (above 31)

Posture 1. Sitting: SIT

2. Standing: STD

Height (M) 1. Short: S (<= 1.70)

2. Medium: M (>1.70-1.81)

3. Tall: T (above 1.81)

For each section, experiment results were analyzed in the following manner. First,

descriptive statistics were provided. Then, correlation analysis, normality test, and outlier

analysis were conducted. Since several dependent variables were considered in this study,

multivariate analysis of variance (MANOVA), using Minitab 17, was conducted in this

study. The MANOVA table provides the following results: Wilks' test, Lawley- Hoteling,

Pillai’s and Roy's test. Analysis of variance (ANOVA) was also conducted. In addition,

linear and non-linear regression models were developed and compared using stepwise

procedures with both forward and backward selections. Forward selection starts with the

assumption of no predictors in the model. It is important to note that because of nature of

the experiment and expected multicollinearity issues, a general linear model were

71

developed using MATLAB 15.Finally, an artificial neural network (ANN) model was

developed using neural network toolbox in MATLAB 15. In addition, an Adaptive

Neural Fuzzy Inference System (ANFIS) using a Sugeno FIS was also developed.

4.2 GENERAL DESCRIPTIVE STATISTICS

Correlations between independent and dependent factors were computed with Minitab

17, by using Pearson product moment since the independent factors were continuous

variables. Then, interval plots were used for better understanding of those relationships.

As expected, correlations existed between physical factors of the human body (e.g., FAC

with HGC, BMI, and Weight). Negative correlations between some independent factors

were noticed such as age and height. Another negative correlation is observed between

MVC and Isotonic endurance limit. A normality test showed that all independent factors

were normally distributed as shown in the graphical plot of normal probabilities. This

indicates no need for any transformation, except in isometric endurance limit cases.

Therefore, a transformation function box (i.e., cox with max Lambda) was applied since

the data are in subgroups. The descriptive statistics were provided in Tables 4-2 and 4-3.

Table 4-2 Overall Summary Data

Variable Mean Standard Dev Minimum Maximum

Age (Y) 41.712 7.833 25.000 65.000

Weight (Kg) 82.60 12.85 55.00 114.00

Height (M) 1.7581 0.0705 1.5500 1.9300

BMI 26.679 3.600 18.711 37.422

HGC (CM) 22.523 1.338 19.500 25.500

FAC (CM) 29.341 2.441 23.000 35.00

72

Table 4-3 Descriptive Statistics (Dependent Factors)

Variable Mean StDev Minimum Maximum

MVC(Kg) 46.718 8.456 17.100 81.600

Isometric End Limit (20%; Sec) 167.45 61.76 60.00 343.00




Isotonic End Limit (20%-60%; Sec) 36.291 17.648 6.000 110.000

4.3 MULTIVARIATE ANALYSIS OF VARIANCE (MANOVA)

Multivariate analysis of variance (MANOVA) with Minitab 17 was conducted with 0.05

significance level, since there are two or more dependent variables. In this experiment,

there are three main tests (MVC, isometric and isotonic muscle fatigues) with nine

detailed independent factors. MANOVA detects and tests the effect of the independent

factor combinations on all dependent factors (responses). The hypothesis is that none of

the nine independent variables has any effect on the four dependent factors (responses).

The MANOVA results are shown in Table 4-4 with significant level of 0.05, indicating

that further analysis by MANOVA is necessary on these significant factors. However, the

research study considers all factors. It shows the significant factors (MVC, Isometric and

Isotonic End, Limit) for each of the analysis for each of the MANOVA tests (i.e., Wilk’s,

Lawley-Hotelling and Pillai’s). Table 6 also demonstrates that further analysis is needed

by ANOVA on the significant factors for better evaluation of effect of each factor on the

responses.

https://en.wikipedia.org/wiki/Dependent_variables

73

Table 4-4 MANOVA for Experiment Terms

Test Factor Wilk’s Lawley-

Hotelling Pillai’s

MVC

Age

0.000 0.000 0.000

Height 0.000 0.000 0.000

Trade 0.036 0.036 0.036

HGC 0.000 0.000 0.000

BMI 0.001 0.001 0.001

FAC 0.00 0.00 0.00

Isometric End, Limit (20%)

(40%) (60%) (80%)

Trade 0.000 0.000 0.000

HGC 0.000 0.000 0.000

FAC 0.000 0.000 0.000

Isotonic End, Limit (20%-

60%)

Age 0.002 0.002 0.002

FAC 0.027 0.027 0.027

Trade .000 .000 .000

HGC 0.019 0.019 0.019

Height 0.036 0.036 0.036

The MANOVA was repeated using all dependent factors as responses and the same

significance factors were obtained as shown in Table 4- 5.

74

Table 4-5 MANOVA for All Dependent Factors

Analysis Factor Wilk’s Lawley-Hotelling Pillai’s

MVC, Isometric and

Isotonic End, Limit

Age 0.000 0.000 0.000

FAC 0.000 0.000 0.000

Trade 0.000 0.000 0.000

HGC 0.000 0.000 0.000

Smoking 0.003 0.003 0.003

Height 0.000 0.000 0.000

BMI 0.000 0.000 0.000

ANOVA has been conducted for each section to identify and confirm the significant

factors. It is found that general linear model (GLM) could not take all categorical

independent variables. Because of the multicollinearity issue, the stepwise GLM

ANOVA is considered as an extra procedure. Significant factors were chosen with

additional prior knowledge about them from the literature taking in consideration that this

data is subjective judgment of participants (human social experiment). Linear regression

models were developed with both general and stepwise methods (to avoid possible

stepwise pitfalls) using MINTAB 17 for each case of MVC. The general linear and

nonlinear regression models were built using the MATLAB 15, considering all factors as

significant.

75

4.4 BASIC ANALYSIS

In the following sections, analysis and discussion of the experiment will be introduced for

all experimental terms, starting by reminding of most related important literature

followed by the descriptive statistics. It also includes general and specific factors, interval

plots, ANOVA with full factorial design of experiment (DOE), and regression analysis.

In the linear regression prediction model, residual analysis plots were generated with

MINTAB 17. MATLAB 15 will be used to find the general linear and nonlinear

regression equations since after initial and thorough review of ANOVA, the literature and

descriptive statists, we decided to include all six independent factors in the predicted

general linear and nonlinear regression equations, specifying equations for each case. In

the section of general descriptive statistics, all experimental terms are included to

describe and summarize data before drawing main conclusions. Interval plots were used

to compare variability intervals for the experimental subjects and summary of central

tendency using a 95% confidence level in different groups of MVC in different

experimental terms. ANOVA has been performed to identify and confirm the significant

factors. Since the general GLM could not take all categorical independent variables, this

dissertation also considered stepwise GLM ANOVA. Significant factors were chosen

with prior knowledge taking in considerations that they are subjective judgment of

participants (human social experiment). Linear regression models were developed for

general as well as stepwise methods to overcome stepwise pitfalls. Using MINTAB 17

for each case of MVC, the general linear and nonlinear regression models were

developed using te MATLAB 15 considering all factors as significant. The standard error

of the regression (S) as a measure of model fit in ANOVA shows lower standard error.

76

The coefficient of determination (R-squared) shows acceptable models that explain and

fit experiment data. Residual plots show that model fit in ANOVA and regression

analysis are satisfactory. The normal probability plot of residuals shows that the

independent variables follow normal distributions since the residues form a straight line.

The plot of residuals against fitted values tests the constant variance assumption and

shows the residuals are on both sides of the graph, with no data points deviating from the

majority of points. Histogram of the residuals shows the general characteristics of

experimental data and plots the residuals that include typical values, spread and shape.

Finally, the relationship between residuals and order of data shows the correlation

between collected data.

4.5 Maximum Voluntary Contraction Analysis and Discussion

The ANOVA results on the MVC experiment data are presented in this section, in

addition to the predicted general linear and nonlinear models for maximums voluntary

contraction (MVC) in different posture (sitting and standing) and both hands (dominant

and non-dominant). Note that different experimental conditions are symbolled as MVC

(Kg, Sit, D), MVC (Kg, Sit, ND), MVC (Kg, Stand, D), MVC (Kg, Stand, ND). ANOVA

with 95% confidence level was used to test the effects of the independent factors.

Hypothesis is presented as none of the experiment independent variables have any effect

on the output dependent variable. Model adequacy checks were tested for MVC data and

found that assumptions are met for constant variance normality and independency. Table

4- 1 shows the dependent factors, independent variables with their levels.

77

The ANOVA using design of experiment with full general factorial regression analysis

was performed using MINTAB 17. Table 4-6 shows ANOVA general factorial regression

ANOVA output.

Table 4- 6 Factor Information for ANOVA General Factorial Regression


Variables

Treatment Levels

1- MVC


Limit (20%, 40%, 60%,

80%)


limit (20-60%)

Fixed Factors



Age (years) 1) A0: (25-<30)

2) A1: (30-<35)

3) A2: (35-<40)

4) A3: (40-<45)

5) A4: (45-<50)

6) A5: Above 50



Instrument


& Navigation

4) Eng: Engine


Equipment

Smoking 1) Smokers

2) Non-smokers

Body Mass Index

(BMI)

1) Small: S (19-<25)

2) Medium: M (25-<30)

3) Large: L (above =>30)

Hand Grip

Circumference (CM)

1) Small: S (=< 21.5)

2) Medium: M (>21.5 -23.5)

3) Large: L (above 23.5)


2) ND: Non-Dominant

Forearm

Circumference (CM)

1) Small: S (<= 27.5)

2) Medium: M (>27.5-31)

3) Large: L (above 31)


2) Standing: STD

78


2) Medium: M (>1.70-1.81)

3) Tall: T (above 1.81)

79

Table 4-7 ANOVA General Factorial Regression

Source DF Adj SS Adj MS F-Value P-Value

Model 95 23613.4 248.562 7.63 0.000

Linear 20 6465.2 323.260 9.93 0.000

Posture 1 31.9 31.855 0.98 0.323

Age (Cat) 5 2323.3 464.653 14.27 0.000

Hand Dominancy (HD) 1 7.4 7.364 0.23 0.635

Trade 4 1575.4 393.842 12.10 0.000

Smoking 1 318.5 318.499 9.78 0.002

Height (CAT) 2 193.7 96.859 2.97 0.052

BMI (Cat) 2 352.7 176.330 5.42 0.005

HGC (Cat) 2 10.4 5.198 0.16 0.852

FAC(Cat) 2 1322.1 661.028 20.30 0.000

2-Way Interactions 75 8435.2 112.470 3.45 0.000

Posture*Age (Cat) 5 24.7 4.941 0.15 0.979

Posture*H D 1 78.4 78.375 2.41 0.122

Posture*Trade 4 76.0 19.008 0.58 0.675

Posture*Smoking 1 6.3 6.319 0.19 0.660

Posture*Height (Cat) 2 24.1 12.055 0.37 0.691

Posture*BMI (Cat) 2 4.0 2.002 0.06 0.940

Posture*HGC (Cat) 2 11.0 5.494 0.17 0.845

Posture*FAC (Cat) 2 14.3 7.131 0.22 0.803

Age (Cat)*Smoking 5 542.2 108.444 3.33 0.006

H D*Smoking 1 233.0 233.000 7.16 0.008

H D*BMI(Cat) 1 143.2 71.610 2.20 0.112

H D*HGC (Cat) 2 17.9 8.926 0.27 0.760

H D*FAC (Cat) 2 123.8 61.906 1.90 0.151

Trade*Smoking 4 1060.6 265.159 8.14 0.000

Trade*Height (Cat) 8 2027.7 253.465 7.78 0.000

Trade*BMI (Cat) 8 1548.0 193.502 5.94 0.000

Smoking*Height (Cat) 2 1101.5 550.756 16.92 0.000

Smoking*BMI (Cat) 2 23.8 11.912 0.37 0.694

Smoking*HGC (Cat) 2 1109.9 554.953 17.04 0.000

Smoking*FAC (Cat) 2 310.5 155.245 4.77 0.009

Height (CAT)*BMI (Cat) 4 2109.6 527.407 16.20 0.000

Height (CAT)*HGC (Cat) 4 861.9 215.466 6.62 0.000

Height (CAT)*FAC (Cat) 4 704.2 176.050 5.41 0.000

BMI (Cat)*HGC (Cat) 4 442.1 110.523 3.39 0.009

Error 432 14065.9 32.560

Lack-of-Fit 150 8350.9 55.673 2.75 0.000

Pure Error 282 5714.9 20.266

Total 527 37679.3

80

ANOVA showed that the significant factors are age, trade, smoking, height, BMI, and

FAC. It also showed that posture, dominancy, and hand grip circumference are non-

significant. Interaction effects are found for age*smoking, HD*smoking, trade*height,

trade*BMI, smoking*height, smoking*BM, smoking*HGC, smoking*FAC, height*BMI,

height*HGC, height*FAC, BMI*HGC. Root Mean Square Error (RMSE) equals 5.7 with

R-sq being 62.67%; however, by nature it is a human social experiment, and non-

significant factors found in this dissertation were found significant in many other studies.

The multicollinearity issue might arise and thus all significant factors are considered.

Then, linear regression equations were calculated for all independent factors. In Figure 4-

1, residual plots consist of normal probability plot, uniform distribution Vs fits, uniform

distribution Vs order, and normal histogram shape distribution.

Figure 4-1 Residual Plots for MVC

81

Regression equations were extracted for both general linear regression and general

nonlinear regression. Table 4-8 shows these equations. Tables 4-9 and 4-10 show general

linear models (MATLAB 15) and general nonlinear models (MATLAB 15), respectively.

Table 4-8 MVC General Linear and Nonlinear Models (MATLAB 15)

Linear

Models

MVC= -21.594 - 0.43487 AGE(Y) + 22.073

HEIGHT (M) - 0.36207 + BMI 0.14221 HGC (CM)

+ 1.8439 FAC (CM)

RMSE: 6.31

R-Sq: 0.448,

R-Sq,(Adj) 0.443

Non

Linear

Models

MVC= 13.786 -0.0051191 * AGE(Y)^2 + 6.0779*

HEIGHT(M)^2 -0.006859 *BMI^2 + 0.0028544*

HGC(CM)^2 + 0.030977* FAC (CM)^2

RMSE: 6.3

R-Sq: 0.451,

R-Sq,(Adj) 0.445

Tables 4-10 and 4-11 show specific detailed grip strength models for each case to enable

comparisons with other researchers who used limited number of independent factors.

Table 4-9 MVC General Linear Models (Detailed) (MATLAB 15) Terms Linear Regression Model Errors

MVC(KG)

(SIT, D)

MVC (KG, SIT, D) = 160.73 - 0.3503 AGE(Y)+ 1.2514

WEIGHT (M) - 77.472 HEIGHT (M) - 4.1551 BMI - 0.082654

HGC (CM) + 1.5912 FGC(CM)

RMSE: 5.89

R-SQ: 0.508

MVC(KG)

(SIT, ND)

MVC (KG, SIT, ND) = 46.365 - 0.48188 AGE(Y) + 0.37769

WEIGHT (M) - 12.922 HEIGHT (M) - 1.5137 BMI -

0.030941HGC (CM) + 1.7817 FGC(CM)

RMSE: 6.13

R-SQ: 0.478

MVC(KG)

(STAND,

D)

MVC (KG, STAND, D) = 223.95 - 0.36711 AGE(Y) + 1.5356

WEIGHT (M) - 120.56 HEIGHT (M) - 5.2587 BMI + 0.087088


RMSE: 5.74

R-SQ: 0.546,

MVC(KG)

(STAND,

ND)

MVC (KG, STAND, ND) = 188.63 - 0.46084 AGE(Y) + 1.2396

WEIGHT (M) - 102.54 HEIGHT (M) - 3.9155 BMI + 0.45526


RMSE: 6.76

R-SQ: 0.415,

82

Table 4-10 MVC General Non Linear Models (detailed) (MATLAB 15) Terms Non- linear Regression Model Errors

MVC(KG)

(SIT, D)

MVC (KG, SIT, D)= 51.669 - 0.0042189 X1^2 + 0.0035843

WEIGHT (M) ^2 - 4.8952 HEIGHT (M) ^2 - 0.039761 BMI^2 -

0.001795 HGC (CM) ^2 + 0.026556 FGC(CM) ^2

RMSE: 5.84

R-SQ: 0.516

MVC(KG)

(SIT, ND)

MVC (KG, SIT, ND)= 29.721 - 0.005548 X1^2 + 0.00098398

WEIGHT (M) ^2 + 1.9134 HEIGHT (M) ^2 - 0.015754 BMI^2 -

0.00083215 HGC (CM) ^2 + 0.029131FGC(CM) ^2

RMSE: 6.2

R-SQ: 0.466,

MVC(KG)

(STAND,

D)

MVC (KG, STAND, D)= 69.781 - 0.004364X1^2 + 0.0042153

WEIGHT (M) ^2 - 12.522 HEIGHT (M) ^2 - 0.050178 BMI^2 +

0.0014772 HGC (CM) ^2 + 0.036575 FGC(CM) ^2

RMSE: 5.69

R-SQ: 0.554,

MVC(KG)

(STAND,

ND)

MVC (KG, STAND, ND)= 61.015 - 0.0054228 X1^2 + 0.0032202

WEIGHT(M) ^2 - 10.816 HEIGHT (M) ^2 - 0.032784 BMI^2 +

0.0095812 HGC (CM) ^2 + 0.027222 FGC(CM) ^2

RMSE: 6.78

R-SQ: 0.412

It is very important to consider all variables and conditions of experiments in comparing

different models since there is no standardized procedure for all experiments (e.g., due to

different apparatuses, subject conditions, loads, etc.). Experimental research intended to

provide a wide range of choices. Some examples of other research outputs are shown in

Figure 4-2 (Chatterjee and Chowdhuri, 1991).

Figure 4-2 MVC Models (Chatterjee & Chowdhuri, 1991)

RMSEs in linear and nonlinear regression models were compered. Table 4-11 shows the

comparison. As shown in Table 4-12, the RMSE (6.125) of the nonlinear model is almost

83

the same as the linear model, i.e., the nonlinear models result in limited improvements

from the linear models, with almost same R-SQ values for both methods.

Table 4–11 RMSE Values for Linear and Nonlinear Regression Models RMSE Linear

Regression

RMSE Non Linear

Regression

R-SQ linear R-SQ Nonlinear

Sitting Posture (Avg) 6.01 6.02 0.49 0.49

Standing Posture (Avg) 6.25 6.23 0.47 0.52

Dominant Hand (Avg) 5.81 5.76 0.52 0.44

Non Dominant Hand

(Avg)

6.445 6.49 0.44 0.48

Avg 6.12875 6.125 0.48 0.4825

In particular, significant factors are extracted for each posture. Regression equations are

provided for both general linear regression and stepwise linear regression equations for

all factors and their interactions. Residual plots support normality assumption, where

only one case was not normal in the isometric calculations. It was normalized by using

cox–box transformation.

In the following paragraph, we examine individual factors in detail. The individual

factors include posture (standing and sitting), age, FAC, GC, smoking status, hand

dominancy, race, and BMI.

Posture (standing and sitting) effect: There is limited literature about posture effect.

Most of aviation work involves standing positions similar to other general trades (such as

fire men, police, and athletics, etc.). Ibarra et al. (2012) mentioned that subjects exerted

more grip strength in the standing position than the sitting position by 3%. They found

that for pinch grip no statistical difference was found between standing and sitting

positions; but key pinch strength was marginally higher for standing and sitting positions.

84

Table 4-12 shows mean MVC values for different age groups with standing and sitting

posture.

Table 4-12 MVC Values for Posture (Standing and Sitting)

Age Group MVC (Avg;

Sitting; DH)

MVC (Avg;

Sitting;

NDH)

MVC (Avg;

Standing; DH)

MVC (Avg;

Standing;

NDH)

Avg

A1: (30- <35) 49.26 49.94 50.8 49.04 49.76

A0: (25-<30) 46.63 50.76 49.98 48.04 48.85

A3: (40-<45) 49.73 46.52 51.43 46.3 48.50

A2: (35-<40) 49.11 46.8 49.17 45.44 47.63

A4: (45-<50) 45.58 44.79 47.49 43.79 45.41

A5: (Above

50) 39.18 36.8 40.73 36.99 38.43

Avg 46.58 45.94 48.27 44.93 46.43

The sitting posture average (46.258 kg) is almost the same as the standing posture

average (46.60 kg). The percentage for both hands in standing/sitting is 1.0073 (0.07%

more); that for the dominant hand in standing/sitting is 1.036 (3.61% more); that for the

dominant hand in standing/sitting is 0.97 (2.02% less). Results agreed with findings of

Ibarra et al. (2012): Subjects exerted more grip strength in the standing position than the

sitting position by 3%. Figures 4-3 and 4-4 show the results for both hands.

85

Figure 4-3 MVC Posture Effect (D)

Figure 4-4 MVC Posture effect (ND)

The general linear equations for MVC with posture effect are as follows:

SIT, MVC (Kg) = -21.72 - 0.4349 Age (Y) + 22.07 Height (M) - 0.362 BMI

+ 0.142 HGC (CM) + 1.844 FAC (CM

STA, MVC (Kg) = -21.47 - 0.4349 Age (Y) + 22.07 Height (M) - 0.362 BMI

+ 0.142 HGC (CM) + 1.844 FAC (CM)

0

10

20

30

40

50

60

70

80

90

1 5 91

31

72

12

52

93

33

74

14

54

95

35

76

16

56

97

37

78

18

58

99

39

71

01

10

51

09

11

31

17

12

11

25

12

9

Max (MVC)Kg Sitting Right

Max (MVC)Kg Standing Right

0

10

20

30

40

50

60

70

80

90

1 5 9

13

17

21

25

29

33

37

41

45

49

53

57

61

65

69

73

77

81

85

89

93

97

10

1

10

5

10

9

11

3

11

7

12

1

12

5

12

9

Max (MVC)Kg Sitting Left

Max (MVC)Kg Standing Left

86

The following paragraphs discuss effects of individual factors including age, trade,

smoking, BMI, hand grip circumference, dominancy, forearm circumference, posture,

and height on MVC.

Age effect: There was a limited number of studies that covered the age groups of 25-29

and 35-50 years old. Most researchers do not agree on the most significant age group,

possibly as a result of different experimental conditions. This dissertation includes age

groups with 5-year age intervals (A0: 25-<30, A1: 30-<35, A2: 35-<40, A3: 40-<45, A4:

45-<50, A5: Above 50. Chatterjee and Chodhuri (1991), Al Meanazel (2013), and

Minnal (2014) considered young ages between 18 and 25 years old. Koley et al. (2009)

considered middle ages between 18 and 40 years old. Bohannon et al. (2006) considered

old ages (75-79, 80-84, 85-89 and 90-99 years old). In all cases, categorizing ages into 5-

year intervals is desirable. Table 4-3 shows the independent variables (e.g., age) with the

six levels, their total counts, mean, and standard deviation. Analysis on MVC finds the

age effect. Asmussen and Heeboll-Nielsen (1955, 1956, and 1962) stated that ages

around 20 years old have a peak MVC amount, which then started to decline with older

ages. Chatterjee and Chodhuri (1991) mentioned that maximum MVC can be achieved by

subjects who are 18 to 22 years old. Anakwe et al. (1995) studied subjects aged 35 to 44

years old. Many other researchers mentioned that MVC is independent of age including

Petrofsky and Linda (1975), which found no effect of aging on isometric muscle strength.

Bohannon et al. (2006) found grip strength inversely proportional with ages. Table 4-14

shows descriptive statistics for the age intervals and maximum MVC exerted by each

interval regarding posture and dominancy. Table 4-13 shows the MVC values for

strongest age groups. Figure 4-5 shows the age effect on MVC.

87

Table 4–13 MVC Values for Strongest Age Groups Highest MVC

Value

Lowest MVC Value

(Kg) Similar Avg (kg)

MVC (KG)

(SIT, D) A3 (40-<45): 49.73 A5 (Above 50): 39.18

A3: (40-<45)

A1: (30-<35)

A2: (35-<40)

46.58

MVC (KG)

(SIT, ND) A0 (25-<30): 50.76 A5 (Above 50): 36.18

A0: (25-<30)

A1: (30-<35) 45.93

MVC (KG)

(STAND,

D)

A3 (40-<45):51.43 A5 (Above 50): 40.73 A3: (40-<45)

A1: (30-<35) 48.26

MVC (KG)

(STAND,

ND)

A1 (30- <35):49.04 A5 (Above 50): 36.99 A1: (30-<35)

A0: (25-<30) 44.93

Figure 4-5 Relationship between MVC and Age for Different Posture and Hand

Dominancy

0

10

20

30

40

50

60

A1

A0

A3

A2

A4

A5

MV

C (

KG

)

AGE PERIOD

(Sitting,D)

(Sitting,(ND)

(Standing,(D)

(Standing,ND)

88

Figure 4-6 Relationship between MVC and Age

This dissertation found that the most significant age period is A1 (30-<35) followed by

A0 (25-<30) with the weakest being A5 (Above 50) as shown in Figure 4-6. The general

linear equations for MVC with age effect are as follows:

A0, MVC (Kg) = -14.62 - 0.723 Age (Y) + 24.43 Height (M) - 0.329 BMI - 0.040 HGC

(CM) + 1.742 FAC (CM)


(CM) + 1.742 FAC (CM)


(CM) + 1.742 FAC (CM)


(CM) + 1.742 FAC (CM)


(CM) + 1.742 FAC (CM)


(CM) + 1.742 FAC (CM)

Height effect: Alex et al. (2013) stated that “a positive association was documented

between height and dominant hand grip strength, while the respective comparison for

weight and dominant hand strength documented a statistically significant positive

association only in the male group”. . Figure 4-7 shows MVC versus height relationships

0

10

20

30

40

50

60

A1

A0

A3

A2

A4

A5

Avg

MV

C (

Kg)

Age(Period)

A1

A0

A3

A2

A4

A5

Avg

89

Figure 4-7 Relationship between MVC and Height

Research found that height has a major effect on MVC where taller people exerted more

MVC with additional 9.1% than medium, and 12.21% than short subjects. The general

linear equations for MVC with height effect are as follows:

S, MVC (Kg) = -37.8 - 0.4342 Age (Y) + 31.57 Height (M) - 0.381 BMI + 0.148 HGC

(CM) + 1.873 FAC (CM)

M, MVC (Kg) = -38.7 - 0.4342 Age (Y) + 31.57 Height (M) - 0.381 BMI + 0.148 HGC

(CM) + 1.873 FAC (CM)

T, MVC (Kg) = -40.1 - 0.4342 Age (Y) + 31.57 Height (M) - 0.381 BMI + 0.148 HGC

(CM) + 1.873 FAC (CM)

BMI effect: Sheriff et al., (2012); Montes (2001); Minnal (2014); Al Meanazel (2013),

and Stulen and De Luca (1981) mentioned that MVC depends on muscle strength and

brain-related factors. Montes (2001) stated that higher muscle diameters were noted in

the maximum voluntary isometric contraction. Stulen and De Luca (1981) mentioned that

MVC value depends on both muscle strength and brain-related factors, different levels of

0

10

20

30

40

50

60

(Sit,D) (Sit,ND) (Stand,D) (Stand,ND)

MV

C (

Kg)

Height Group

Tall

Meduim

Short

90

grip strength holding time do not vary between weaker and stronger individuals when

subjected to the same load. This might be due to loads used being small or the condition

of experiment itself. Figure 4-8 shows MVC versus BMI relationships.

Figure 4-8 Relationship between MVC and BMI

This research showed that greater MVC is exerted by subjects with medium BMI, and

highest MVC is exerted in MVC (Kg, Stand, D) condition. In all cases, we do not find

relation between strong or weak muscles and BMI. Results show being the medium BMI

is most likely to have better MVC values that being in other categories. The general

linear equations for MVC with BMI effect are as follows:

L, MVC (Kg) = -20.03 - 0.4386 Age (Y) + 21.31 Height (M) - 0.432 BMI + 0.264 HGC

(CM) + 1.803 FAC (CM)


(CM) + 1.803 FAC (CM)

40

42

44

46

48

50

52

large Meduim Short

MV

C (

Kg)

BMI

(Sit,D)

(Sit,ND)

(Stand,D)

(Stand,ND)

91


(CM) + 1.803 FAC (CM)

Hand grip circumference (HGC) effect: This research found that there is a strong

correlation between hand volume and maximum MVC. Minnal (2014) and Al Meanazel

(2013) mentioned that higher grip circumference exerted more MVC. Other researchers

obtained different values for MVC (lower or higher) and similar for all other percentages.

Figure 4-8 shows the relationship between MVC and HGC. This research found that

subjects exerted more MVC when they have larger HGC, and the highest MVC is exerted

in MVC (Kg, Stan, D) condition. The general linear equations for MVC with HGC effect

are as follows:

L, MVC (Kg) = 15.0 - 0.4379 Age (Y) + 24.16 Height (M) - 0.349 BMI - 1.343 HGC

(CM) + 1.727 FAC (CM)

M, MVC (Kg) = 11.1 - 0.4379 Age (Y) + 24.16 Height (M) - 0.349 BMI - 1.343 HGC

(CM) + 1.727 FAC (CM)

S, MVC (Kg) = 8.6 - 0.4379 Age (Y) + 24.16 Height (M) - 0.349 BMI - 1.343 HGC

(CM) + 1.727 FAC (CM)

Forearm grip circumference (FAC) effect: There were a very limited number of

studies focusing on forearm circumference for both dominant and non-dominant hands.

Anakwe et al. (2007) stated that “Forearm circumference generally decreased with age

for both men and women, although this decline was less marked for women”. Fraser et al.

(1999) also mentioned that “British subjects have slightly greater values for dominant

forearm circumference measurements in both men and women (29.1 cm Vs 24.3 cm for

men and 25.6 cm vs. 20.4 cm for women”. Kallman et al. (1990) found that forearm

circumference delivered the best practical method for assessing the MVC grip strength

92

and muscle mass for both genders. Figure 4- 9 shows the relationship between MVC and

FAC.

Figure 4-9 Relationship between FAC and MVC

The general linear equations for isometric for MVC with FAC effect are as follows:

L, MVC (Kg) = -27.9 - 0.4366 Age (Y) + 22.05 Height (M) - 0.354 BMI + 0.135 HGC

(CM) + 2.036 FAC (CM)


(CM) + 2.036 FAC (CM)


(CM) + 2.036 FAC (CM).

Trade effect: There was no literature taking in consideration the effect of different trades

on MVC. This dissertation examined the trade effect on MVC for five jobs in aviation

trade: APG: Airplane Genera, E&I: Electrical and Instrument, COMNAV:

Communication & Navigation, Eng: Engine and GSE: Ground Support Equipment. It

also tested smoking effect with two levels (smokers and non-smokers). Figure 4-10

shows the relationship between MVC and trades.

0

10

20

30

40

50

60


MV

C(k

g)

Posture & Dominancy

large

Meduim

Small

93

Figure 4-10 Relationship between Trade and MVC for Different Posture and

Dominancy

This research found that all trades mostly exerted the same MVC; however, the highest

MVC was exerted by Eng and E & I trades which is 37 for mean ages of 42 year old (the

most significant age range) and the highest MVC is exerted in MVC (Kg, Stand, D)

condition. The general linear equations for isometric for MVC with trade effect are as

follows:

APG: MVC (Kg) = -24.66 - 0.4309 Age (Y) + 20.40 Height (M) - 0.437 BMI

+ 0.465 HGC (CM) + 1.892 FAC (CM)

COMNAV: MVC (Kg) = -24.94 - 0.4309 Age (Y) + 20.40 Height (M) - 0.437 BMI

+ 0.465 HGC (CM) + 1.892 FAC (CM)

E&I: MVC (Kg) = -25.56 - 0.4309 Age (Y) + 20.40 Height (M) - 0.437 BMI

+ 0.465 HGC (CM) + 1.892 FAC (CM)

ENG: MVC (Kg) = -26.66 - 0.4309 Age (Y) + 20.40 Height (M) - 0.437 BMI

+ 0.465 HGC (CM) + 1.892 FAC (CM)

GSE: MVC (Kg) = -26.03 - 0.4309 Age (Y) + 20.40 Height (M) - 0.437 BMI

+ 0.465 HGC (CM) + 1.892 FAC (CM)

0

10

20

30

40

50

60

APG COMNAV E&I ENG GSE

MV

C(k

g)

Trade

(Sit,D)

(Kg,Sit,ND)

(Stand,D)

(Stand,ND)

94

Race effect: Tables 4-14, 4-15 provides descriptive statistics and summary of MVC

values for different races. Figures 4-11 shows MVC Vs race relationships. Those values

cannot be used for comprehensive comparisons, since more information about

experimental subjects and anthropometric data are needed in all cases. The effect of race

on MVC depends on many factors, such as culture (especially for women), physical

factors, age, etc.

Table 4-14 Descriptive Statistics for Jordanian Subjects Variable Mean StDev Minimum Maximum

Age (Y) 41.712 7.833 25.000 65.000

Weight (Kg ) 82.60 12.85 55.00 114.00

Height (M) 1.7581 0.0705 1.5500 1.9300

BMI 26.679 3.600 18.711 37.422

HGC(CM 22.523 1.338 19.500 25.500

FAC (CM) 29.341 2.441 23.000 35.00

Table 4-15 Descriptive Statistics: MVC Values for Different Races Population MVC (Kg)

(Males)

MVC (Kg)

(Females)

Author(s) (Year)

Singaporean

24.1 N/A Incel et al. (2002)

Indian

30-39.8 22.75 Vaz et al. (1998, 2002), Koley et al.

(2009)

Jordan (Pilot Study)

33.619 N/A Al-Momani (2015)

Spanish

39.95 25.72 Heredia et al. (2005)

Scotland

35.12 23.02 Heredia et al. (2005)

Scotland 40.0–48.8 27.5–34.4 Brenner et al. (1989)

Jordan 46.58167 N/A Al-Momani (2015)

USA 62.0 37.0 Crosby and Wehbe (1994)

USA 44.8 35.0 Al Meanazel (2013)

95

Figure 4-11 Relationship between MVC and Race (Male)

The subject group (from 25 to 60 years old) showed the following means: age (41.71

years old), weight (82.6 Kg), height (1.75 m), BMI (26.67), hand grip circumference

(22.52 cm), forearm circumference (29.34 cm), and MVC (46.58 kg). In general, the race

factor is very important since it is related to culture, life style, and physical factors in

general. Most studies focusing on the relationship between MVC and race were

performed in USA and UK, whereas a very limited number of studies was found for the

race effect worldwide.

Smoking Effect: Most researchers such as Isaac and Rand (1969) said that smoking

leads to profound vasoconstriction, results in tissues starving of nutritive blood and

bypassing from arterioles to venules. Asano and Branemark (1970) said that nicotine

levels increase in plasma up to 10 micrograms per 100 mm of blood. Sorensen et al.

(2009) mentioned that smoking workers’ capabilities decreased because of lung

incapacity to provide more oxygen to muscles. Asano and Branemark (1970), Isaac and

0

10

20

30

40

50

60

70

Singap

ore

an

Ind

ian

Jord

an (P

ilot Stu

dy)

Span

ish

Scotlan

d

Scotlan

d

Jord

an

USA

USA

MV

C9

Kg)

Race

MVC (Kg) (Males)

96

Rand (1969); Davis (1960) and Al Meanazel (2013) found that non-smokers can exert

more force. Figure 4-12 shows the relationship between MVC and smoking.

Figure 4-12 Relationship between MVC and Smoking

This research found that smokers exerted more MVCs in sitting than those in standing by

2%, and exerted more MVC using the dominant hand by extra 5.52% for the strongest

age group. Also, highest MVC was exerted in MVC (Kg, Stand, D) condition. All

researchers connected smoking with lower performance in all aspects. This research

finding disagreed with most researchers and might be related to nature of the experiment

and age of smoking subjects as well as the fact that two thirds of all subjects are smokers.

The general linear equations for MVC with smoking effect are as follows:

NS, MVC (Kg) = -20.94 - 0.4369 Age (Y) + 21.66 Height (M) - 0.379 BMI + 0.142 HGC

(CM) + 1.875 FAC (CM)


(CM) + 1.875 FAC (CM)

0

20

40

60

80

100

120


MV

C (

kg)

Smoking (Posture & Dominancy)

S

NS

97

Dominancy effect: Dominancy has been classified into two levels (dominant and non-

dominant). Armstrong and Oldham (1999) stated that “dominant hand is significantly

stronger than non-dominant hand”. Ibarra et al. (2012) stated that the dominant hand is

stronger by 0.1–3% than the non-dominant hand in right-handed people and very little

difference in hand dominancy is found in left-handed people. Incel et al. (2002) and

Bohannon et al. (2006) mentioned that the dominant hand is stronger by 3.9%. Bohannon

et al. (2006), Koley et al. (2009), Sorensen et al. (2009), and Al Meanazel (2013) agreed

that the dominant hand is stronger by 10.9% and 33.3% for both hands, and the dominant

right hand is stronger than the dominant left hand. But other researchers such as Incel et

al. (2002) stated that “there is no difference between dominant and non-dominant hand”

on MVC values and found that no difference in grip strength between left- and right-

handed persons. Figure 4-13 shows the relationship between MVC and dominancy. There

were 122 subjects with the dominant hand being the right hand and 10 subjects being left-

hand dominant.

Figure 4-13 Relationship between Hand Dominancy and MVC for Different Age

Groups, Hand Dominancy, and Posture

Research results showed that:

1- In general, the dominant hand exerted more MVC than the non-dominant hand for

all age groups. Max MVC in sitting (D) is 46.58 kg; Max MVC in standing (D) is

0

10

20

30

40

50

60

A1

A0

A3

A2

A4

A5

MV

C (

KG

)

HAND DOMINANCY EFFECT

(Sitting),(DH)

(Sitting),(NDH)

(Standing),(DH)

(Standing),(NDH)

98

48.26 kg; Max MVC in sitting (ND) is 45.93 kg; and Max MVC in standing (ND)

is 44.93 kg.

2- Dominant hand in standing posture exerts more MVC than that in sitting posture

by 3.6%.

3- Non-dominant hand in sitting posture exerts more MVC than that in standing

posture by 2.2%.

4- Dominant hand in sitting posture exerts more MVC than non-dominant hand by

1.4%.

5- Dominant hand in standing posture exerts more MVC than non-dominant hand

by 7.41%

6- Other factors do not have significant effects on MVC.

7- The highest MVC was exerted by the dominant hand of subjects aged 30-45 years

old, followed by those aged 25-30 years old and above 45 years old.

8- For the non-dominant hand, the younger subjects (25-30 years old) exerted most

MVC, followed by those aged 30-35 and above 35 years old. The reason might be

that at younger ages both hands have almost the same strength; however, as

getting older, the subjects use the dominant hands more frequently which become

stronger. The general linear equations for isometric for MVC with dominancy

effect are as follows:

D, MVC (Kg) = -22.69 - 0.4302 Age (Y) + 22.60 Height (M) - 0.352 BMI + 0.174 HGC

(CM) + 1.813 FAC (CM)

ND, MVC (Kg) = -24.42 - 0.4302 Age (Y) + 22.60 Height (M) - 0.352 BMI

+ 0.174 HGC (CM) + 1.813 FAC (CM).

99

4.6 Isometric Endurance Limit: Analysis and Discussion

During the last decades, studies on MVC isometric and isotonic endurance limits used

different fractions of MVC (5%, 10%, 15%, 20%, 30%, 40%, 60%, 80%). There were no

experimental standardizations which make it difficult to compare. The fractions of 25%,

50%, and 75% of the MVC were tested, which were changed to 20%, 40%, 60%, and

80% for comparisons with latest studies during the last five years. ANOVA results are

presented in this section, in addition to the predicted general linear and nonlinear models

for isometric endurance limit. Table 4-16 shows the dependent factors, and independent

variables with their levels. Tables 4-17, 4-18, 4-19 and 4-20 show the general linear

equations for isometric endurance limit with age effect.

Table 4-16 Factor Information for ANOVA General Factorial Regression


Variables

Treatment Levels

1- MVC


Limit (20%, 40%, 60%,

80%)


limit (20-60%)

Fixed Factors



Age (years) 1) A0: (25-<30)

2) A1: (30-<35)

3) A2: (35-<40)

4) A3: (40-<45)

5) A4: (45-<50)

6) A5: Above 50



Instrument


& Navigation

4) Eng: Engine


Equipment

Smoking 1) Smokers

2) Non-smokers

100

Body Mass Index

(BMI)

1) Small: S (19-<25)

2) Medium: M (25-<30)

3) Large: L (Above 30)

Hand Grip

Circumference (CM)

4) Small: S (=< 21.5)

5) Medium: M (21.5-<23.5)

6) Large: L (Above 23.5)


2) ND: Non-Dominant

Forearm

Circumference (CM)

1) Small: S (<= 27.5)

2) Medium: M (>27.5-31)

3) Large: L (Above 31)


2) Standing: STD


2) Medium: M (1.70-<1.81)

3) Tall: T (Above 1.81)

101

Table 4-17 ANOVA General Factorial Regression: Isometric En 20% Source DF Adj SS Adj MS F-Value P-Value

Model 95 1775.90 18.6937 6.67 0.000

Linear 20 552.01 27.6005 9.85 0.000

Posture 1 0.00 0.0000 0.00 1

Age (Cat) 5 79.38 15.8756 5.66 0.000

Hand Dominancy (HD) 1 81.75 81.7499 29.16 0,000

Trade 4 177.74 44.4352 15.85 0.000

Smoking 1 6.31 6.3086 2.25 0.134

Height(Cat) 2 50.47 25.2370 9.00 0.000

BMI (Cat) 2 13.20 6.6024 2.36 0.096

HGC (Cat) 2 18.68 9.3384 3.33 0.007

FAC(Cat) 2 67.18 33.5908 11.98 0.000


Posture*Age (Cat) 5 0.00 0.0000 0.00 1.000

Posture*H D 1 0.00 0.0000 0.00 1.000

Posture*Trade 4 0.00 0.0000 0.00 1.000

Posture*Smoking 1 0.00 0.0000 0.00 1.000

Posture*Height ( Cat) 2 0.00 0.0000 0.00 1.000

Posture*BMI (Cat) 2 0.00 0.0000 0.00 1.000

Posture*HGC (Cat) 2 0.00 0.0000 0.00 1.000

Posture*FAC (Cat) 2 0.00 0.0000 0.00 1.000

Age (Cat)*Smoking 5 23.26 4.6527 1.66 0.143

H D*Smoking 1 4.07 4.0691 1.45 0.229

H D*BMI (Cat) 2 36.41 18.2043 6.49 0.002

H D*HGC (Cat) 2 93.33 46.6663 16.65 0.000

H D*FAC (Cat) 2 55.98 27.9919 9.99 0.000

Trade*Smoking 4 33.88 8.4699 3.02 0.018

Trade*Height (Cat) 8 86.50 10.8122 3.86 0.000

Trade*BMI (Cat) 8 54.08 6.7599 2.41 0.015

Smoking*Height (Cat) 2 10.93 5.4672 1.95 0.143

Smoking*BMI (Cat) 2 17.23 8.6145 3.07 0.047

Smoking*HGC (Cat) 2 2.60 1.3003 0.46 0.629

Smoking*FAC (Cat) 2 10.87 5.4357 1.94 0.145

Height (CAT)*BMI

(Cat)

4 50.07 12.5164 4.47 0.002

Height (CAT)*HGC

(Cat)

4 73.01 18.2528 6.51 0.000

Height (Cat)*FAC (Cat) 4 75.88 18.9691 6.77 0.000

BMI (Cat)*HGC (Cat) 4 201.16 50.2899 17.94 0.000

Error 432 1210.90 2.8030

Lack-of-Fit 150 1044.17 11.77 6.9611

Pure Error 282 166.74 0.5913

Total 527 2986.80

102

Table 4-18 ANOVA General Factorial Regression: Isometric En 40% Source DF Adj SS Adj MS F-Value P-Value

Model 95 81.750 0.86053 7.46 0.000

Linear 20 23.414 1.17068 10.15 0.000

Posture 1 0.000 0.00000 0.00 1.000

Age (Cat) 5 4.945 0.98908 8.57 0.000

H D 1 0.705 0.70485 6.11 0.014

Trade 4 2.188 0.54694 4.74 0.001

Smoking 1 4.509 4.50856 39.08 0.000

Height( Cat) 2 1.473 0.73673 6.39 0.002

BMI (Cat) 2 0.045 0.02271 0.20 0.821

HGC (Cat) 2 2.463 1.23149 10.68 0.000

FAC (Cat) 2 2.375 1.18731 10.29 0.000


Posture*Age (Cat) 5 0.000 0.00000 0.00 1.000

Posture*H D 1 0.000 0.00000 0.00 1.000

Posture*Trade 4 0.000 0.00000 0.00 1.000

Posture*Smoking 1 0.000 0.00000 0.00 1.000

Posture*Height (Cat) 2 0.000 0.00000 0.00 1.000

Posture*BMI (Cat) 2 0.000 0.00000 0.00 1.000

Posture*HGC (Cat) 2 0.00 0.000 0.00000 1.000

Posture*FAC (Cat) 2 0.00 0.000 0.00000 1.000

Age (Cat)*Smoking 5 0.913 0.18262 1.58 0.164

H D*Smoking 1 2.942 2.94158 25.50 0.000

H D*BMI(Cat) 2 1.194 0.59700 5.18 0.006

H D*HGC (Cat) 2 0.204 0.10207 0.88 0.414

H D*FAC(Cat) 2 2.056 1.02809 8.91 0.000

Trade*Smoking 4 4.249 1.06219 9.21 0.000

Trade*Height(Cat) 8 3.085 0.38565 3.34 0.001

Trade*BMI(Cat) 8 3.274 0.40926 3.55 0.001

Smoking*Height(Cat) 2 0.665 0.33266 2.88 0.057

Smoking*BMI (Cat) 2 1.988 0.99401 8.62 0.000

Smoking*HGC (Cat) 2 0.161 0.08053 0.70 0.498

Smoking*FAC (Cat) 2 0.200 0.09998 0.87 0.421

Height (Cat)*BMI (Cat) 4 6.801 1.70037 14.74 0.000

Height (Cat)*HGC (Cat) 4 1.698 0.42454 3.68 0.006

Height (Cat)*FAC(Cat) 4 4.320 1.07996 9.36 0.000

BMI (Cat)*HGC (Cat) 4 2.570 0.64254 5.57 0.000

Error 432 49.834 0.11536

Lack-of-Fit 150 44.457 0.29638 15.54 0.000

Pure Error 282 5.378 0.01907

Total 527 527 131.585

103

Table 4-19 ANOVA General Factorial Regression: Isometric En 60% Source DF DF Adj SS Adj MS F-Value P-Value

Model 95 95 99.224 1.04447 7.23 0.000

Linear 20 20 21.385 1.06927 7.40 0.000

Posture 1 1 0.000 0.00000 0.00 1.000

Age (Cat) 5 5 2.987 0.59732 4.13 0.001

H D 1 1 1.651 1.65103 11.43 0.001

Trade 4 4 2.394 0.59849 4.14 0.003

Smoking 1 1 5.254 5.25394 36.37 0.000

Height (Cat) 2 2 0.954 0.47688 3.30 0.038

BMI (Cat) 2 2 0.703 0.35143 2.43 0.089

HGC (Cat) 2 2 0.876 0.43786 3.03 0.049

FAC (Cat) 2 2 2.481 1.24047 8.59 0.000

2-Way Interactions 75 75 64.169 0.85559 5.92 0.000

Posture*Age (Cat) 5 5 0.000 0.00000 0.00 1.000

Posture*H D 1 1 0.000 0.00000 0.00 1.000

Posture*Trade 4 4 0.000 0.00000 0.00 1.000

Posture*Smoking 1 1 0.000 0.00000 0.00 1.000

Posture*Height (Cat) 2 2 0.000 0.00000 0.00 1.000

Posture*BMI (Cat) 2 2 0.000 0.00000 0.00 1.000

Posture*HGC (Cat) 2 2 0.000 0.00000 0.00 1.000

Posture*FAC (Cat) 2 2 0.000 0.00000 0.00 1.000

Age (Cat)*Smoking 5 5 10.355 2.07101 14.34 0.000

H D*Smoking 1 1 5.166 5.16588 35.76 0.000

H D*BMI (Cat) 2 2 1.055 0.52767 3.65 0.027

H D*HGC (Cat) 2 2 0.050 0.02504 0.17 0.841

H D*FAC (Cat) 2 2 3.489 1.74446 12.07 0.000

Trade*Smoking 4 4 5.241 1.31027 9.07 0.000

Trade* Height (Cat) 8 8 1.746 0.21822 1.51 0.151

Trade*BMI (Cat) 8 8 4.146 0.51828 3.59 0.000

Smoking* Height (Cat) 2 2 1.215 0.60774 4.21 0.016

Smoking*BMI (Cat) 2 2 0.461 0.23031 1.59 0.204

Smoking*HGC (Cat) 2 2 0.488 0.24401 1.69 0.186

Smoking*FAC (Cat) 2 2 0.070 0.03501 0.24 0.785

Height(Cat)*BMI(Cat) 4 4 8.722 2.18058 15.09 0.000

Height (Cat)*HGC (Cat) 4 4 1.149 0.28715 1.99 0.095

Height (Cat)*FAC (Cat) 4 4 1.535 0.38386 2.66 0.032

BMI (Cat)*HGC (Cat) 4 4 7.372 1.84292 12.76 0.000

Error 432 432 62.411 0.14447

Lack-of-Fit 150 150 56.831 0.37888 19.15 0.000

Pure Error 282 282 5.580 0.01979

Total 527 527 161.635

104

Table 4-20 ANOVA General Factorial Regression: Isometric En 80% Source DF DF Adj SS Adj MS F-Value P-Value

Model 95 95 1.30229 0.013708 9.16 0.000

Linear 20 20 0.24234 0.012117 08.09 0.000

Posture 1 1 0.00000 0.000000 0.00 1.000

Age (Cat) 5 5 0.03101 0.006203 4.14 0.001

H D 1 1 0.01802 0.018019 12.04 0.001

Trade 4 4 0.06961 0.017402 11.62 0.000

Smoking 1 1 0.03737 0.037370 24.96 0.000 Height(Cat) 2 2 0.01194 0.005968 3.99 0.019

BMI (Cat) 2 2 0.00331 0.001655 1.11 0.332

HGC (Cat) 2 2 0.03104 0.015522 10.37 0.000

FAC (Cat) 2 2 0.01052 0.005258 3.51 0.031

2-Way Interactions 75 75 0.73273 0.009770 6.53 0.000

Posture*Age (Cat) 5 5 0.00000 0.000000 0.00 1.000

Posture*H D 1 1 0.00000 0.000000 0.00 1.000

Posture*Trade 4 4 0.00000 0.000000 0.00 1.000

Posture*Smoking 1 1 0.00000 0.000000 0.00 1.000

Posture*Height (CAT) 2 2 0.00000 0.000000 0.00 1.000

Posture*BMI (Cat) 2 2 0.00000 0.000000 0.00 1.000

Posture*HGC(Cat) 2 2 0.00000 0.000000 0.00 1.000

Posture*FAC (Cat) 2 2 0.00000 0.000000 0.00 1.000

Age (Cat)*Smoking 5 5 0.10127 0.020254 13.53 0.000

H D*Smoking 1 1 0.03778 0.037778 25.23 0.000

H D*BMI (Cat) 2 2 0.01071 0.005353 3.58 0.029

H D*HGC (Cat) 2 2 0.00104 0.000518 0.35 0.708

H D*FAC (Cat) 2 2 0.02276 0.011381 7.60 0.001

Trade*Smoking 4 4 0.08695 0.021738 14.52 0.000

Trade*Height (Cat) 8 8 0.06235 0.007794 5.21 0.000

Trade*BMI (Cat) 8 8 0.06225 0.007781 5.20 0.000

Smoking* Height(Cat) 2 2 0.03354 0.016769 11.20 0.000

Smoking*BMI (Cat) 2 2 0.00820 0.004100 2.74 0.066

Smoking*HGC (Cat) 2 2 0.00639 0.003197 2.14 0.119

Smoking*FAC (Cat) 2 2 0.00444 0.002219 1.48 0.228

Height (Cat)*BMI (Cat) 4 4 0.07743 0.019358 12.93 0.000

Height (Cat)*HGC (Cat) 4 4 0.02671 0.006677 4.46 0.002

Height (Cat)*FAC (Cat) 4 4 0.01718 0.004296 2.87 0.023

BMI (Cat)*HGC (Cat) 4 4 0.08177 0.020443 13.65 0.000

Error 432 432 0.64677 0.001497

Lack-of-Fit 150 150 0.59516 0.003968 21.68 0.000

Pure Error 282 282 0.05161 0.000183

Total 527 527 1.94906

Tables 4-21 and 4-22 show the significant and non-significance factors with the two-

factor interactions; but as mentioned before, the experiment is a human social

105

experiment. Many studies found the above non-significant factors to be significant.

Linear regression equations were derived for all independent factors.

Table 4-21 Significant Factors Found with ANOVA

Significant Factors Non-significant

Factors

Model

Summary

Isometric En

20%

AGE, HD, Trade, Height,

HGC, FAC

Smoking BMI

Posture

S :1.67

R-sq : 59.46%

Isometric En

40%


HGC, FAC, Smoking BMI Posture

S :0.339

R-sq : 62.13%

Isometric En

60%

AGE, Posture, HD, Trade,

HGC, FAC, Smoking BMI, Height

S :0.38

R-sq : 61.39%

Isometric En

80%


HGC, FAC, Smoking BMI Posture

S :0.038

R-sq : 66.82%

106

Table 4-22 ANOVA Interaction Factors Interaction Factors Model Summary

Isometric

Endurance

Limit (20%)

H D*BMI,

H D*HGC,

H D*FAC,

Trade*Height(Cat),

Trade*BMI,

Smoking*Height,

Smoking*BMI,

Height*BMI,

Height*HGC,

Height*FAC, BMI*HGC

S: 1.67

R-sq: 59.46%

Isometric

Endurance

Limit (40%)

H D*FAC,

Trade*Smoking,

Trade*Height(Cat),

Trade*BMI,

Smoking*Height,

Smoke*BMI,

Height*BMI,

Height*HGC ,

Height*FAC, BMI*HGC

S: 0.339

R-sq: 62.13%

Isometric

Endurance

Limit (60%)

Age*Smoking,

H D*Smoking,

H D*BMI,

H D*FAC,

Trade*Smoking,

Trade*BMI, Trade*BMI,

Smoking*Height,

Height*BMI, BMI*HGC

S :0.38

R-sq : 61.39%

Isometric

Endurance

Limit (80%)

Age*Smoking,

H D*Smoking,

H D*BMI,

H D*HGC,

H D*FAC,

Trade*Smoking,

Trade*Height( CAT),

Trade*BMI,

Smoking*Height,

Smoking*BMI,

Smoking*HGC,

Smoking*FAC, Height*BMI,

Height*HGC, Height*FAC, BMI*HGC

S :0.038

R-sq : 66.82%

107

Figure 4-14 Residual plots for isometric endurance limit test

General linear and nonlinear models for isometric endurance limit at 20%, 40%, 60% and

80% of the MVC are derived by including all experimental factors in the model, using the

MATLAB 15 as shown in Tables 4-23 and 4-24. They overcome the multicollinearity

problems appeared through data analyses using MINTAB 17. The linear models will be

compared with other statistical software’s results. The isometric endurance limit test has

been conducted for the sitting position with the dominant hand to compare with others

studies.

Table 4-23 Isometric Endurance Limit General Linear Regression Models

Linear Regression Model Errors

Isometric Endurance

Limit (20%)

127.25 + -0.0014601 * AGE(Y)^2 + -37.427

*HEIGHT(M)^2 -0.05763*BMI^2 +

0.084164*HGC(CM)^2 + 0.18183*FAC

(CM)^2

RMSE: 58.4

R-Sq: 0.116

R-Sq(Adj) 0.107

Isometric Endurance

Limit (40%)

177.76 -0.0072556* AGE(Y)^2 -

18.581*HEIGHT(M)^2 -0.035377*BMI^2 -

0.16351*HGC(CM)^2 + 0.086309*FAC

(CM)^2

RMSE: 33.5

R-Sq: 0.117

R-Sq(Adj): 0.109

108

Isometric Endurance

Limit (60%)

83.723-0.0016898 b2* AGE(Y)^2 -

8.1407*HEIGHT(M)^2 -0.024757*BMI^2 -

0.088919*HGC(CM)^2 + 0.05318*FAC

(CM)^2

RMSE: 21

R-Sq: 0.0835,

R-Sq(Adj):

0.0747

Isometric Endurance

Limit (80%)

44.498 + 0.0016698* AGE(Y)^2 -

2.8629*HEIGHT(M)^2 -0.011467*BMI^2 -

0.072948*HGC(CM)^2 + 0.032936*FAC

(CM)^2

RMSE: 13.1

R-Sq: 0.0891,

R-Sq(Adj):

0.0804

Isometric Endurance

Limit (Avg)

108.31 -0.0021839* AGE(Y)^2

16.753*HEIGHT(M)^2 -0.032308*BMI^2 -

0.060302 *HGC(CM)^2 + 0.088564*FAC

(CM)^2

RMSE: 23.8

R-Sq: 0.109

R-Sq(Adj): 0.101

Table 4-24 Isometric Endurance Limit Nonlinear Regression Models Non-inear Regression Model Errors

Isometric

Endurance

Limit (20%)

127.25 + -0.0014601 * AGE(Y)^2 -37.427

*HEIGHT(M)^2 + -0.05763*BMI^2 +

0.084164*HGC(CM)^2 + 0.18183*FAC (CM)^2

RMSE: 58.4

R-Sq: 0.116

R-Sq(Adj):

0.107 Isometric

Endurance

Limit (40%)

177.76 -0.0072556* AGE(Y)^2 -

18.581*HEIGHT(M)^2 -0.035377*BMI^2 -

0.16351*HGC(CM)^2 + 0.086309*FAC (CM)^2

RMSE: 33.5

R-Sq: 0.117

R-Sq(Adj):

0.109 Isometric

Endurance

Limit (60%)

83.723-0.0016898 b2* AGE(Y)^2 -

8.1407*HEIGHT(M)^2 -0.024757*BMI^2 -

0.088919*HGC(CM)^2 + 0.05318*FAC (CM)^2

RMSE: 21

R-Sq: 0.0835,

R-Sq(Adj):

0.0747 Isometric

Endurance

Limit (80%)

44.498 + 0.0016698* AGE(Y)^2 -

2.8629*HEIGHT(M)^2 -0.011467*BMI^2 -

0.072948*HGC(CM)^2 + 0.032936*FAC (CM)^2

RMSE: 13.1

R-Sq: 0.0891,

R-Sq(Adj):

0.0804 Isometric

Endurance

Limit (Avg)

108.31 + -0.0021839* AGE(Y)^2 -

6.753*HEIGHT(M)^2 -0.032308*BMI^2 -

0.060302*HGC(CM)^2 + 0.088564*FAC (CM)^2

RMSE: 23.8

R-Sq: 0.109

R-Sq(Adj):

0.101

109

Table 4-25 Isometric Endurance Limit RMSE Values or Linear and Nonlinear

Regression Models

Condition

RMSE Linear

Regression

(Matlab)

RMSE Non

Linear Regression

(Matlab)

R- Sq

linear

(Minitab)

R- Sq

nonlinear

(Minitab)

Isometric Endurance Limit

(20%) 59.6 59.5 0.117 0.119


(40%) 34 34.2 0.129 0.0917


(60%) 21.5 21.5 0.082 0.0844


(80%) 13.4 13.3 0.083 0.0945


(Avg)

32.1 32.1 0.102 0.0974

Table 4-26 shows RMSE Values for both linear and nonlinear regression for isometric

endurance limits. The nonlinear model results in an average RMSE of 32.1, which is

almost the same as that for the linear model, and did not result in a more accurate model.

Also, its R-squared value is around 0.0974. The following paragraphs discuss effects of

individual factors including age, trade, smoking, BMI, hand grip circumference, hand

dominancy, forearm circumference, posture, and height on isometric endurance limit.

Age effect: Age has been classified into six age intervals and this classification could

identify isometric endurance limit differences between age intervals more accurately.

Table 4-26 shows the mean of isometric endurance limit for different age groups.

According to Chatterjee and Chowdhury (1991), no effect of aging was observed on

isometric muscle strength. Yassierli et al. (2003) found that at fraction of 40% of MVC,

isometric endurance limit is independent of gender and age. Bohannon et al. (2006) found

interactive effects of different factors (gender and age) with effort level have significant

influence on fatigue, and grip strength is inversely proportional with aging. Table 4-27

110

shows means of isometric endurance limit for different age groups. Figure 4-14 shows

age effect on isometric endurance limit.

Table 4–26 Means of Isometric Endurance Limit for Different Age Groups

Age group

Isometric

Endurance

Limit (20%)

Isometric

Endurance

Limit (40%)

Isometric

Endurance

Limit (60%)

Isometric

Endurance

Limit (80%)

Isometric

Endurance

Limit (Avg)

A0: (25-<30) 159.2 111.1 45.56 24.22 85.02

A1: (30-<35) 176.4 86.91 49.45 29.3 85.515

A3: (40-<45) 159.73 61.27 32.79 19.05 68.21

A2: (35-<40) 163.22 76.52 42.07 21.52 75.8325

A4: (45-<50) 177.3 68.07 34.6 19.65 74.905

A5: (Above

50) 158.9 75.67 45 30.11

77.42

Avg 165.79167 79.923333 41.578333 23.975 77.817083

Figure 4-15 Relationship between Isometric Endurance Limit and Age

The mean isometric endurance limit decreases as the fraction of the MVC increases

(20%: 167.5 Seconds, 40%: 73.12 Seconds, 60%: 38.37 Seconds, 80%: 21.75 Seconds).

Two age groups (A0 (25-<30) and A1 (30-<35)) exerted the highest isometric mean

0

20

40

60

80

100

120

140

160

180

200

A0

A1

A3

A2

A4

A5

ISO

MET

RIC

END

, LIM

IT (

SEC

)

AGE PERIOD

20%

40%

60%

80%

111

endurance limit (85 Seconds), followed by older ages. Results for fraction grip strength

relationship agreed with Yassierli et al. (2003) where he stated that interactive effects of

age and gender and the effort level have a significant influence on fatigue. It disagreed

with Petrofsky and Linda (1975) where no effect of aging was found in isometric muscle

strength for subjects with a very wide range of ages (between 22 and 60 years old). The

general linear equations for isometric endurance limit with age effect are as follows:

Regression Equation (20%)

A0 Isometric Endurance Limit (20%) = 39.7 + 1.57 Age (Y) - 150.1 Height (M) -

3.307 BMI+ 4.59 HGC (CM) + 11.50 FAC (CM)


3.307 BMI+ 4.59 HGC (CM) + 11.50 FAC (CM)


3.307 BMI+ 4.59 HGC (CM) + 11.50 FAC (CM)


3.307 BMI + 4.59 HGC (CM) + 11.50 FAC (CM)


3.307 BMI+ 4.59 HGC (CM) + 11.50 FAC (CM)

A5 Isometric Endurance Limit (20%) = -22.9 + 1.57 Age (Y) - 150.1 Height (M) -

3.307 BMI+ 4.59 HGC (CM) + 11.50 FAC (CM)


A0 Isometric Endurance Limit (40%) = 300.6 - 2.580 Age (Y) - 25.4 Height (M) -

1.036 BMI- 6.49 HGC (CM) + 3.117 FAC (CM)


1.036 BMI- 6.49 HGC (CM) + 3.117 FAC (CM)


1.036 BMI- 6.49 HGC (CM) + 3.117 FAC (CM)


1.036 BMI 6.49 HGC (CM) + 3.117 FAC (CM)

112


1.036 BMI- 6.49 HGC (CM) + 3.117 FAC (CM)


1.036 BMI - 6.49 HGC (CM) + 3.117 FAC (CM)


A0 Isometric Endurance Limit (60%) = 144.5 - 1.771 Age (Y) + 0.1 Height (M) -

0.880 BMI- 4.039 HGC (CM) + 1.976 FAC (CM)


0.880 BMI- 4.039 HGC (CM) + 1.976 FAC (CM)


0.880 BMI - 4.039 HGC (CM) + 1.976 FAC (CM)


0.880 BMI- 4.039 HGC (CM) + 1.976 FAC (CM)


0.880 BMI - 4.039 HGC (CM) + 1.976 FAC (CM)


0.880 BMI - 4.039 HGC (CM) + 1.976 FAC (CM)



0.568 BMI - 2.894 HGC (CM) + 1.795 FAC (CM)


0.568 BMI - 2.894 HGC (CM) + 1.795 FAC (CM)


0.568 BMI- 2.894 HGC (CM) + 1.795 FAC (CM)


0.568 BMI- 2.894 HGC (CM) + 1.795 FAC (CM)


0.568 BMI- 2.894 HGC (CM) + 1.795 FAC (CM)


0.568 BMI- 2.894 HGC (CM) + 1.795 FAC (CM)

113

Height effect: There are a very limited of studies that examined the effect of height on

isometric endurance limit. Chatterjee and Chowdhuri (1991) and Caldwell (1963) found

no relationship between height and isometric endurance limit. Figure 4-16 shows the

relationship between isometric endurance limit and height.

Figure 4-16 Relationship between Isometric Endurance Limit and Height

The effect of height on isometric endurance limit is insignificant. However, subjects with

medium to tall height achieve higher endurance limits, especially in isometric endurance

limit (20%) condition. For other conditions, they almost have the same effect. The

general linear equations for isometric endurance limit with height effect are as follows:


M Isometric Endurance Limit (20%) = 229 - 0.019 Age (Y) - 207.9 Height (M) -

3.243 BMI + 3.38 HGC (CM) + 10.96 FAC (CM)

S Isometric Endurance Limit (20%) = 207 - 0.019 Age (Y) - 207.9 Height (M) -

3.243 BMI + 3.38 HGC (CM) + 10.96 FAC (CM)

T Isometric Endurance Limit (20%) = 224 - 0.019 Age (Y) - 207.9 Height (M) -

3.243 BMI + 3.38 HGC (CM) + 10.96 FAC (CM)

0

20

40

60

80

100

120

140

160

180

200

20

%

40

%

60

%

80

%

Iso

me

tric

En

du

ran

ce L

imit

(Se

c)

MVC Percentage

Tall

Meduim

Short

114


M Isometric Endurance Limit (40%) = 434.4 - 0.813 Age (Y) - 151.2 Height (M) -

1.668 BMI - 7.07 HGC (CM) + 4.846 FAC (CM)

S Isometric Endurance Limit (40%) = 424.8 - 0.813 Age (Y) - 151.2 Height (M) -

1.668 BMI - 7.07 HGC (CM) + 4.846 FAC (CM)

T Isometric Endurance Limit (40%) = 445.0 - 0.813 Age (Y) - 151.2 Height (M) -

1.668 BMI- 7.07 HGC (CM) + 4.846 FAC (CM)



1.097 BMI - 3.909 HGC (CM) + 2.839 FAC (CM)


1.097 BMI - 3.909 HGC (CM) + 2.839 FAC (CM)

T Isometric Endurance Limit (60%) = 290.1 - 0.236 Age (Y) - 112.2 Height (M) -

1.097 BMI - 3.909 HGC (CM) + 2.839 FAC (CM)


M Isometric Endurance Limit (80%) = 129.8 + 0.0754 Age (Y) - 44.0 Height (M) -

0.493 BMI- 3.199 HGC (CM) + 1.775 FAC (CM)

S Isometric Endurance Limit (80%) = 124.7 + 0.0754 Age (Y) - 44.0 Height (M) -

0.493 BMI - 3.199 HGC (CM) + 1.775 FAC (CM)

T Isometric Endurance Limit (80%) = 132.8 + 0.0754 Age (Y) - 44.0 Height (M) -

0.493 BMI - 3.199 HGC (CM) + 1.775 FAC (CM)

BMI effect: BMI effect on isometric endurance limit has been studied by Crosby and

Wehbe (1994), Fraser et al. (1999), Montes (2001), Sheriff et al., (2012), Al Meanazel

(2013), and Minnal (2014). There is a positive correlation between physical factors and

isometric endurance limits. Figure 4-16 shows the relationship between isometric

endurance limit and BMI.

115

Figure 4-17 Relationship between Isometric Endurance Limit and BMI

The effect of BMI on isometric endurance limit is insignificant. Isometric endurance

limits of subjects with small and medium BMIs are greater than those by subjects with

large BMIs by 8.83% (overall average). Large BMIs are associated with weakest readings

in three isometric endurance limit test conditions (40%, 60% and 80%) The condition of

20% results in more endurance than others. Results agreed with Funderburk et al. (1974)

who found a positive correlation between higher body physical factors (forearm

anthropometric BMI and hand muscle) with hand grip strength. Chatterjee and

Chowdhuri (1991) stated that holding time did not vary between persons with high and

low BMIs for isometric strength at 15% of the MVCs. The general linear equations for

isometric endurance limit with BMI effect are as follows:


L Isometric Endurance Limit (20%) = -17.4 - 0.148 Age (Y) - 117.3 Height (M) -

0.13 BMI+ 3.05 HGC (CM) + 10.78 FAC (CM)

M Isometric Endurance Limit (20%) = -6.2 - 0.148 Age (Y) - 117.3 Height (M) -

0.13 BMI + 3.05 HGC (CM) + 10.78 FAC (CM)

0

100

200

300

400

500

600

20% 40% 60% 80%

Iso

me

tric

En

du

ran

ce L

imit

(Se

c)

MVC (Percentage)

S

M

L

116


0.13 BMI+ 3.05 HGC (CM) + 10.78 FAC (CM)


L Isometric Endurance Limit (40%) = 92.7 - 0.878 Age (Y) - 55.1 Height (M)

+ 3.81 BMI - 7.44 HGC (CM) + 4.914 FAC (CM)

M Isometric Endurance Limit (40%) = 125.0 - 0.878 Age (Y) - 55.1 Height (M)

+ 3.81 BMI - 7.44 HGC (CM) + 4.914 FAC (CM)

S Isometric Endurance Limit (40%) = 152.1 - 0.878 Age (Y) - 55.1 Height (M)

+ 3.81 BMI- 7.44 HGC (CM) + 4.914 FAC (CM)


L Isometric Endurance Limit (60%) = 50.3 - 0.281 Age (Y) - 25.3 Height (M)

+ 0.997 BMI - 3.649 HGC (CM) + 2.849 FAC (CM)

M Isometric Endurance Limit (60%) = 67.2 - 0.281 Age (Y) - 25.3 Height (M)

+ 0.997 BMI - 3.649 HGC (CM) + 2.849 FAC (CM)

S Isometric Endurance Limit (60%) = 74.6 - 0.281 Age (Y) - 25.3 Height (M)

+ 0.997 BMI - 3.649 HGC (CM) + 2.849 FAC (CM)


L Isometric Endurance Limit (80%) = 45.5 + 0.0472 Age (Y) - 9.7 Height (M)

+ 0.071 BMI- 2.928 HGC (CM) + 1.708 FAC (CM)

M Isometric Endurance Limit (80%) = 52.0 + 0.0472 Age (Y) - 9.7 Height (M)

+ 0.071 BMI- 2.928 HGC (CM) + 1.708 FAC (CM)

S Isometric Endurance Limit (80%) = 52.3 + 0.0472 Age (Y) - 9.7 Height (M)

+ 0.071 BMI- 2.928 HGC (CM) + 1.708 FAC (CM)

Hand grip circumference effect: Minnal (2014) and Al Meanazel (2013) found that

subjects with higher grip circumferences achieved more endurance limit. Figure 4-17

shows the relationship between isometric endurance limit and HGC.

117

Figure 4-18 Relationship between Isometric Endurance Limit and HGC

The effect of HGC on isometric endurance limit is little. Greater HGC values are exerted

by subjects in the medium range. Large HGC values were observed in the following

conditions: 20%, 40%, 60% of the MVCs but not in the 80% condition, which might be

because of the nature of the experiment. Generally, larger HGCs can exert larger

isometric endurance limits. The general linear equations for isometric endurance limit

with HGC effect are as follows:

Regression Equation HGC (20%)

L Isometric Endurance Limit (20%) = 73 - 0.156 Age (Y) - 127.6 Height (M) -

3.025 BMI + 3.96 HGC (CM) + 10.72 FAC (CM)

M Isometric Endurance Limit (20%) = 77 - 0.156 Age (Y) - 127.6 Height (M) -

3.025 BMI + 3.96 HGC (CM) + 10.72 FAC (CM)

S Isometric Endurance Limit (20%) = 74 - 0.156 Age (Y) - 127.6 Height (M) -

3.025 BMI + 3.96 HGC (CM) + 10.72 FAC (CM)

0

20

40

60

80

100

120

140

160

180

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20

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40

%

60

%

80

%

Iso

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ura

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Lim

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Sec)

MVC Percentage

L

M

S

118


L Isometric Endurance Limit (40%) = 447.8 - 0.901 Age (Y) - 61.9 Height (M) -

1.617 BMI - 13.79 HGC (CM) + 4.621 FAC (CM)


1.617 BMI - 13.79 HGC (CM) + 4.621 FAC (CM)


1.617 BMI - 13.79 HGC (CM) + 4.621 FAC (CM)



1.098 BMI - 9.31 HGC (CM) + 2.665 FAC (CM)

Isometric Endurance Limit (60%) = 255.4 - 0.297 Age (Y) - 23.2 Height (M) -

1.098 BMI - 9.31 HGC (CM) + 2.665 FAC (CM)


1.098 BMI - 9.31 HGC (CM) + 2.665 FAC (CM)


L Isometric Endurance Limit (80%) = 48.2 + 0.0499 Age (Y) - 11.7 Height (M) -

0.499 BMI - 2.39 HGC (CM) + 1.901 FAC (CM)


0.499 BMI - 2.39 HGC (CM) + 1.901 FAC (CM)


0.499 BMI - 2.39 HGC (CM) + 1.901 FAC (CM)

Forearm circumference effect: There is also a limited number of studies investigating

the effect of FAC on isometric endurance limits. Anakwe et al. (2007) stated that

“Forearm circumference generally decreased with age for both men and women, although

this decline was less marked for women”. Figure 4-19 shows the relationship between

isometric endurance limit and FAC.

119

Figure 4-19 Relationship between Isometric Endurance Limit and FAC

Subjects with large FGCs exerted more isometric endurance limit for all fractions of the

MVCs percentages than those with medium and small FGCs. The general linear

equations for isometric endurance limit with FAC effect are as follows:


L Isometric Endurance Limit (20%) = -156 - 0.243 Age (Y) - 128.0 Height (M) -

2.850 BMI+ 3.71 HGC (CM) + 17.80 FAC (CM)

M Isometric Endurance Limit (20%) = -128.9 - 0.243 Age (Y) - 128.0 Height (M) -

2.850 BMI+ 3.71 HGC (CM) + 17.80 FAC (CM)

S Isometric Endurance Limit (20%) = -99.8 - 0.243 Age (Y) - 128.0 Height (M) -

2.850 BMI+ 3.71 HGC (CM) + 17.80 FAC (CM)



1.774 BMI- 7.02 HGC (CM) + 4.99 FAC (CM)


1.774 BMI- 7.02 HGC (CM) + 4.99 FAC (CM)

0

20

40

60

80

100

120

140

160

180

ISOMETRICEND, LIMIT

(20%)

ISOMETRICEND, LIMIT

(40%)

ISOMETRICEND, LIMIT

(60%)

ISOMETRICEND, LIMIT

(80%)

avg

Iso

me

tric

En

du

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ce L

imit

(Se

c)

MVC Percentage

APG

COMNAV

E&I

avg

120


1.774 BMI- 7.02 HGC (CM) + 4.99 FAC (CM)



1.193 BMI- 3.857 HGC (CM) + 3.17 FAC (CM)


1.193 BMI- 3.857 HGC (CM) + 3.17 FAC (CM)


1.193 BMI- 3.857 HGC (CM) + 3.17 FAC (CM)


L Isometric Endurance Limit (80%) = 98.7 + 0.0910 Age (Y) - 8.9 Height (M) -

0.509 BMI

- 3.205 HGC (CM) + 0.876 FAC (CM)


0.509 BMI

- 3.205 HGC (CM) + 0.876 FAC (CM)


0.509 BMI

- 3.205 HGC (CM) + 0.876 FAC (CM)

Trade effect: A very limited number of studies take in consideration the effect of

different trades on isometric endurance limit. This dissertation examined the trade effect

on isometric endurance limit for aviation trades with five levels (APG: Airplane General,

E & I: Electrical and Instrument, COMNAV: Communication & Navigation, Eng:

Engine, and GSE: Ground Support Equipment). Figure 4-20 shows the relationship

between isometric endurance limit and trade.

121

Figure 4-20 Relationship between Isometric Endurance Limit and Trade

Subjects in APG and Eng trades achieved greater values in isometric endurance limit and

E& I whereas those in COMNAV achieve the lowest due to their nature of work. The

general linear equations for isometric endurance limit with trade effect are as follows:


APG Isometric Endurance Limit (20%) = 144.7 - 0.267 Age (Y) - 93.5 Height (M) -

1.753 BMI - 2.07 HGC (CM) + 9.76 FAC (CM)

COMNAV Isometric Endurance Limit (20%) = 128.0 - 0.267 Age (Y) - 93.5 Height (M)

- 1.753 BMI- 2.07 HGC (CM) + 9.76 FAC (CM)

E&I Isometric Endurance Limit (20%) = 153.5 - 0.267 Age (Y) - 93.5 Height (M) -

1.753 BMI- 2.07 HGC (CM) + 9.76 FAC (CM)

ENG Isometric Endurance Limit (20%) = 179.0 - 0.267 Age (Y) - 93.5 Height (M) -

1.753 BMI - 2.07 HGC (CM) + 9.76 FAC (CM)

GSE Isometric Endurance Limit (20%) = 109.8 - 0.267 Age (Y) - 93.5 Height (M) -

1.753 BMI - 2.07 HGC (CM) + 9.76 FAC (CM)

0

50

100

150

200

250

AP

G

CO

MN

AV

E&I

Engin

e

GSE

ISO

MET

RIC

EN

DU

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MIT

(se

c)

MVC Percentage

20%

40%

60%

80%

122



1.567 BMI - 4.19 HGC (CM) + 4.123 FAC (CM)

COMNAV Isometric Endurance Limit (40%) = 224.3 - 0.928 Age (Y) - 65.3 Height (M)

- 1.567 BMI- 4.19 HGC (CM) + 4.123 FAC (CM)


1.567 BMI - 4.19 HGC (CM) + 4.123 FAC (CM)


1.567 BMI - 4.19 HGC (CM) + 4.123 FAC (CM)


1.567 BMI - 4.19 HGC (CM) + 4.123 FAC (CM)



1.167 BMI- 2.184 HGC (CM) + 2.612 FAC (CM)

COMNAV Isometric Endurance Limit (60%) = 96.6 - 0.293 Age (Y) - 28.7 Height (M) -

1.167 BMI - 2.184 HGC (CM) + 2.612 FAC (CM)


1.167 BMI - 2.184 HGC (CM) + 2.612 FAC (CM)


1.167 BMI- 2.184 HGC (CM) + 2.612 FAC (CM)


1.167 BMI - 2.184 HGC (CM) + 2.612 FAC (CM)


APG Isometric Endurance Limit 80%) = 54.6 + 0.0483 Age (Y) - 12.0 Height (M) -

0.560 BMI - 1.880 HGC (CM) + 1.606 FAC (CM)

COMNAV Isometric Endurance Limit (80%) = 46.9 + 0.0483 Age (Y) - 12.0 Height (M)

- 0.560 BMI - 1.880 HGC (CM) + 1.606 FAC (CM)

E&I Isometric Endurance Limit (80%) = 47.7 + 0.0483 Age (Y) - 12.0 Height (M) -

0.560 BMI- 1.880 HGC (CM) + 1.606 FAC (CM)

123

ENG Isometric Endurance Limit (80%) = 47.1 + 0.0483 Age (Y) - 12.0 Height (M) -

0.560 BMI- 1.880 HGC (CM) + 1.606 FAC (CM)

GSE Isometric Endurance Limit (80%) = 54.7 + 0.0483 Age (Y) - 12.0 Height (M) -

0.560 BMI - 1.880 HGC (CM) + 1.606 FAC (CM)

Race effect: There is a very limited number of studies investigating race effect. In this

dissertation, all experimental subjects were Jordanian. This could be considered as a

baseline for future studies that include different races, and as good comparisons for

middle-east studies. Table 4-27 shows anthropometric Data for Jordanian subjects. Table

4-28 shows descriptive statistics of experimental results on isometric endurance limit

from this experiment.

Table 4-27 Anthropometric Data for Jordanian Subjects

Variable Mean Standard

Deviation

Minimum Maximum

Age (Y) 41.712 7.833 25.000 65.000

Weight(Kg ) 82.60 12.85 55.00 114.00

Height (M) 1.7581 0.0705 1.5500 1.9300

BMI 26.679 3.600 18.711 37.422

HGC(CM) 22.523 1.338 19.500 25.500

FAC (CM) 29.341 2.441 23.000 35.00

124

Table 4-28 Descriptive Statistics: Isometric Endurance Limit


Isometric Endurance

Limit (20%)

167.45

61.94 60.00 343.00

Isometric Endurance

Limit (40%)

73.12

35.61 21.00 203.00

Isometric Endurance

Limit (60%)

38.37

21.90 9.00 116.00

Isometric Endurance

Limit (80%)

21.75 13.66 5.00 93.00

Smoking effect: Most researchers found that non-smokers can exert more force (Asano

and Branemark 1970; Isaac and Rand 1969; Davis 1960, and Al Meanazel 2013). Figure

4-21 shows the relationship between isometric endurance limit and smoking status.

Figure 4-21 Relationship between Isometric Endurance Limit and Smoking

On average, smokers exerted more isometric endurance limit than non-smokers by

12.98%. This might be because of (1) nature of the experiment where only medium to

0

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(SE

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MVC PERCENTAGE

Smoking

Non Smoking

125

low loads are studied, and (2) the mean age of the smokers. The general linear equations

for isometric endurance limit with smoking effect are as follows:

NS Isometric Endurance Limit (20%) = 60.8 - 0.083 Age (Y) - 114.9 Height (M) -

2.617 BMI + 3.77 HGC (CM) + 9.85 FAC (CM)


2.617 BMI+ 3.77 HGC (CM) + 9.85 FAC (CM)


1.539 BMI - 6.97 HGC (CM) + 4.567 FAC (CM)


1.539 BMI - 6.97 HGC (CM) + 4.567 FAC (CM)


1.139 BMI - 3.813 HGC (CM) + 2.881 FAC (CM)


1.139 BMI- 3.813 HGC (CM) + 2.881 FAC (CM)

NS Isometric Endurance Limit (80%) = 67.2 + 0.0629 Age (Y) - 9.6 Height (M) -

0.520 BMI - 3.137 HGC (CM) + 1.818 FAC (CM)


0.520 BMI- 3.137 HGC (CM) + 1.818 FAC (CM)

Dominancy effect: Many researchers stated that isometric endurance limit for the

dominant hand is greater than the non-dominant hand. For example, Chatterjee and

Chowdhuri (1991) found that the dominant hand exerted the same load for a longer

period of time (15 seconds) than the non-dominant hand. Al Meanazel (2013) stated that

the dominant hand has the highest endurance limit. Figure 4-22 shows the relationship

between isometric endurance limit and dominancy. Note that there were 122 subjects

with a dominant right hand and 10 subjects with a dominant left hand.

126

Figure 4-22 Relationship between Isometric Endurance Limit and Dominancy

Results were very clear. Subjects have higher isometric endurance limit with the

dominant hand (3.56%) than the non-dominant one. The findings agreed with the

literature. The general linear equations for isometric endurance limit with dominancy


D Isometric Endurance Limit (20%) = 89.1 - 0.184 Age (Y) - 131.2 Height (M) -

3.163 BMI + 3.45 HGC (CM) + 10.98 FAC (CM)

ND Isometric Endurance Limit (20%) = 106.4 - 0.184 Age (Y) - 131.2 Height (M) -

3.163 BMI + 3.45 HGC (CM) + 10.98 FAC (CM)


1.765 BMI - 6.89 HGC (CM) + 4.960 FAC (CM)


1.765 BMI- 6.89 HGC (CM) + 4.960 FAC (CM)


1.199 BMI - 3.757 HGC (CM) + 2.974 FAC (CM)


1.199 BMI- 3.757 HGC (CM) + 2.974 FAC (CM)

0

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20

%

40

%

60

%

80

%

ISO

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(SE

C)

MVC PERCENTAGE

Dominant

Non Dominant

127

D Isometric Endurance Limit (80%) = 64.1 + 0.0761 Age (Y) - 8.1 Height (M) -

0.490 BMI- 3.047 HGC (CM) + 1.731 FAC (CM)

ND Isometric Endurance Limit (80%) = 59.3 + 0.0761 Age (Y) - 8.1 Height (M) -

0.490 BMI

- 3.047 HGC (CM) + 1.731 FAC (CM)

4.7 Isotonic Endurance Limit Analysis and Discussion

The result of ANOVA performed on the isotonic endurance limit (20-60%) experimental

results are presented in this section. In addition, the predicted general linear and nonlinear

models for isotonic endurance limit were developed. ANOVA with 95% confidence level

was used to test the effects of independent factors. Also, different hypothesis-testing and

model adequacy checks were conducted. In particular, model assumptions of constant

variance, normality and independency were evaluated. Table 4-1 shows the dependent

factors, and independent variables with their levels. The ANOVA using design of

experiment with full general factorial regression analysis was performed with MINTAB

17. Table 4-29 shows outputs from ANOVA general factorial regression.

128

Table 4-29 ANOVA General Factorial Regression Source DF Adj SS Adj MS F-Value P-Value

Model 95 78633 827.71 4.18 0.000

Linear 20 16721 836.06 4.22 0.000

Posture 1 48 48.18 0.24 0.622

Age (Cat) 5 8073 1614.66 8.16 0.000

H D 1 740 740.04 3.74 0.054

Trade 4 4326 1081.47 5.46 0.000

Smoking 1 2281 2280.69 11.52 0.001

Height (Cat) 2 926 462.84 2.34 0.098

BMI (Cat) 2 755 377.73 1.91 0.150

HGC (Cat) 2 3298 1649.00 8.33 0.000

FAC (Cat) 2 248 123.80 0.63 0.535

2-Way Interactions 75 51746 689.94 3.49 0.000

Posture*Age (Cat) 5 763 152.59 0.77 0.571

Posture*H D 1 103 103.05 0.52 0.471

Posture*Trade 4 1765 441.37 2.23 0.065

Posture*Smoking 1 89 89.00 0.45 0.503

Posture*Height (Cat) 2 140 70.07 0.35 0.702

Posture*BMI (Cat) 2 434 217.12 1.10 0.335

Posture*HGC (Cat) 2 730 365.21 1.85 0.159

Posture*FAC (Cat) 2 2 0.89 0.00 0.995

Age (Cat) *Smoking 5 811 162.29 0.82 0.536

H D*Smoking 1 425 425.36 2.15 0.143

H D*BMI (Cat) 1 475 237.56 1.20 0.302

H D*HGC (Cat) 2 3938 1969.04 9.95 0.000

H D*FAC (Cat) 2 503 251.34 1.27 0.282

Trade*Smoking 4 3802 950.43 4.80 0.001

Trade*Height (Cat) 8 5814 726.75 3.67 0.000

Trade*BMI (Cat) 8 6070 758.75 3.83 0.000

Smoking*Height (Cat) 2 1166 583.14 2.95 0.054

Smoking*BMI (Cat) 2 82 41.14 0.21 0.812

Smoking*HGC (Cat) 2 1537 768.67 3.88 0.021

Smoking*FAC (Cat) 2 4780 2390.18 12.08 0.000

Height (Cat)*BMI (Cat) 4 2517 629.35 3.18 0.014

Height (Cat)*HGC (Cat) 4 1266 316.42 1.60 0.174

Height (Cat)*FAC (Cat) 4 3955 988.68 5.00 0.001

BMI (Cat)*HGC (Cat) 4 5350 1337.42 6.76 0.000

Error 432 85501 197.92

Lack-of-Fit 150 49087 327.25 2.53 0.000

Pure Error 282 36414 129.13

Total 527 164134

Figure 4-23 shows four residual plots: normal probability plot, the uniform distribution

against fits, uniform distribution against order, and normal histogram shape distribution.

129

Figure 4-23 Residual Plots for Isotonic Endurance Limit

Regression equations were derived for both general linear regression and general non-

linear regression models. Table 4-30 shows general linear models (MATLAB 15) and

general nonlinear models (MATLAB 15), respectively.

Table 4-30 Isotonic Endurance Limit Linear and Nonlinear Models

Model Model Summary

Linear Models

Isotonic Endurance Limit = 29.495 + 0.15947AGE(Y) -

55.559 HEIGHT (M) -0.57916 BMI + 3.558 HGC (CM) +

1.1294 FAC (CM)

RMSE: 16.9

R-Sq: 0.0914,

R-Sq,(Adj) 0.0827

Nonlinear Models

Isotonic Endurance Limit = 33.635 + 0.0018137*

AGE(Y)^2 -16.255*HEIGHT(M)^2 -0.010432 *BMI^2 +

0.077773*HGC(CM)^2 + 0.0204*FAC (CM)^2

RMSE: 16.9

R-Sq: 0.0939,

R-Sq,(Adj) 0.0852

130

In Tables 4-31, 4-32 and 4-33 specific detailed grip strength models are shown for each

experimental condition, which enable us to compare with other researchers.

Table 4-31 Isotonic Endurance Limit General Linear Models (MATLAB 15) Condition Linear Regression Model Errors

Isotonic

Endurance

20-60% LS,

RH

10.447 + 0.21693 AGE (Y) - 0.2437 WEIGHT(KG) - 15.791

HEIGHT (M) + 0.69384 BMI + 1.5815 HGC (CM) + 0.42891

FAC (CM)

RMSE: 15.7

R- SQ: 0.0493

Isotonic

Endurance

20-60%

HS, RH

407.28 + 0.35501 AGE (Y) + 2.1465 WEIGHT(KG) - 266.84

HEIGHT (M) - 7.4043 BMI + 1.3116 HGC (CM) + 2.4362 FAC

(CM)

RMSE: 14.1

R- SQ: 0.179

Isotonic

Endurance

20-60% HS,

LH

155.86, - 0.01367, AGE (Y) + 1.1824, WEIGHT(KG) - 168.78

HEIGHT (M) - 4.3126, BMI + 7.9354, HGC (CM) + 0.76883,

FAC (CM)

RMSE: 20.7

R- SQ: 0.183

Isotonic

Endurance

20-60% LS,

LH

65.081, + 0.1381 AGE (Y) + 0.1627 WEIGHT(KG) - 67.117

HEIGHT (M) - 1.1717 BMI + 3.3002 HGC (CM) + 0.72653

FAC (CM)

RMSE: 13.6

R- SQ: 0.102

Table 4-32 Isotonic Endurance Limit General Nonlinear Models (MATLAB) Condition Non Linear Equation Errors

Isotonic

Endurance 20-

60% LS, RH

40.518 + 0.0025783 AGE (Y) ^2 - 0.00030012 WEIGHT(KG)^2

- 9.9457 HEIGHT(M)^2 + 0.0020933 BMI^2 + 0.034526

HGC(CM)^2 + 0.008021 FAC (CM)^2

RMSE: 15.7

R- SQ: 0.0514

Isotonic

Endurance 20-

60% HS,

RH

133.28 + 0.0045011 AGE (Y) ^2 + 0.0065015 WEIGHT(KG)^2

- 47.88 HEIGHT(M)^2 - 0.076118 BMI^2 + 0.026819

HGC(CM)^2 + 0.042281 FAC (CM)^2

RMSE: 14

R- SQ: 0.191

Isotonic

Endurance 20-

60% HS,

LH

46.61 - 0.00062459 AGE (Y) ^2 + 0.003113 WEIGHT(KG)^2 -

31.131 HEIGHT(M)^2 - 0.041001 BMI^2 + 0.17612

HGC(CM)^2 + 0.013446 FAC (CM)^2

RMSE: 20.6

R- SQ: 0.185

Isotonic

Endurance 20-

60% LS,

LH

37.953 + 0.0015277 AGE (Y) ^2 + 0.00015033

WEIGHT(KG)^2 - 15.986 HEIGHT(M)^2 - 0.014257 BMI^2

+ 0.071276 HGC(CM)^2 + 0.014364 FAC (CM)^2

RMSE: 13.6

R- SQ: 0.104

Table 4-33 RMSE for Isotonic Endurance Limit in Linear and Nonlinear Regression Condition

Isotonic Endurance

20-60%

RMSE Linear

Regression

(Matlab)

RMSE Non Linear

Regression(Matlab)

R- SQUARED

linear (Minitab)

R- SQUARED

nonlinear (Minitab)

LS, RH 15.7 15.7 0.0493 0.0514

HS, RH 14.1 14 0.179 0.191

HS, LH 20.7 20.6 0.183 0.185

lS, LH 13.6 13.6 0.102 0.104

Avg 16.025 15.975 0.12833 0.13285

131

The following paragraphs discuss effects of individual factors on isotonic endurance limit

including age, Trade, Smoking, BMI, Hand Grip Circumference, Dominancy, Forearm

Circumference, Posture, and Height.

Age effect: There is a limited number of studies about age effect on isotonic endurance

limit (20% -60% of MVC). Age has been classified into six intervals. According to the

literature review, subjects of all ages ranging from 10 to 99 years old have been studied.

Chatterjee and Chodhuri (1991), and Minnal (2014) and Al Meanazel (2013) considered

young ages between 18 and 25 years old. Koley et al. (2009) considered middle ages

(between 18 and 40 years old). Bohannon et al. (2006) considered old ages with ranges

from 75-79, 80-84, and 85-89 to 90-99 years old. In all cases, ages are classified based on

5-year ranges. Tables 4-34 and 4-35 showed the descriptive statistics and summary of

isotonic endurance, respectively.

Table 4-34 Descriptive Statistics of Isotonic Endurance Limit


Isotonic Endurance Limit,

20-60% LS, RH

38.32 15.72 6.00 110.00


20-60%, LS, RH

33.73 15.18 9.00 80.00


20-60%, HS, LH

42.45 22.33 9.00 109.00


20-60%, LS, LH

30.67 13.99 7.00 85.00

Table 4-35 Summary of Isotonic Endurance Limit Test Regarding Age

Age group LS, RH HS, RH HS, LH LS, LH Avg

A0: (25-<30) 31.67 27.22 29.89 36.64 31.355

A1: (30- <35) 37.55 39.45 48.73 28.24 38.493

A3: (40-<45) 38.88 28.3 43.09 27.63 34.475

A2: (35-<40) 36.63 32.85 37.3 33.16 34.985

A4: (45-<50) 38.47 35.56 46.58 36.22 39.208

A5: (Above 50) 48.22 47 40.78 30.11 41.528

Avg 38.57 35.06 41.06 32 36.674

132

Figure 4-24 Relationship between Age and Isotonic Endurance Limit for Different

Speed and Dominancy

The effect of age on isotonic endurance limit is observed for both conditions of (1) low

and high speed, and (2) left and right hand. Older subjects aged above 50 exerted the

most isotonic endurance limits whereas those with the youngest ages have the lowest

isotonic endurance limit. Findings for fraction grip strength agreed with Yassierli et al.

(2003), who stated that “interactive effects of age, gender, and effort level have

significant influence on fatigue and for grip strength relationship”. It disagreed with

Petrofsky and Linda (1975), who found no effect of age on isometric muscle strength.

The general linear equations for isotonic endurance limit with age effect are as follows:

A0 Isotonic Endurance Limit 20-60% l = 21.7 + 0.302 Age (Y) - 59.9 Height (M) -

0.669 BMI + 3.800 HGC (CM) + 1.358 FAC (CM)


0.669 BMI + 3.800 HGC (CM) + 1.358 FAC (CM)


0.669 BMI + 3.800 HGC (CM) + 1.358 FAC (CM)


0.669 BMI+ 3.800 HGC (CM) + 1.358 FAC (CM)


0.669 BMI + 3.800 HGC (CM) + 1.358 FAC (CM)

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A 0 A 1 A 3 A 2 A 4 A 5

ISO

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AGE PERIOD (SPEED & DOMINANCY)

LS,D

LS,ND

HS,D

HS,ND

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0.669 BMI + 3.800 HGC (CM) + 1.358 FAC (CM)

Height effect: There are a very limited number of studies that investigated the effect of

height on isotonic endurance limit. Figure 4-25 shows the relationship between height

and isotonic endurance limit.

Figure 4-25 Relationship between Height and Isotonic Endurance Limit

This dissertation examined the effect of height on isotonic endurance limit. Results show

that subjects with medium height exerted more isotonic endurance limit than other height

categories. The reason may be because they have the highest MVC and in good health.

The general linear equations for isotonic endurance limit with height effect are as

follows:

S: Isotonic Endurance Limit 20%-60% l = 25.7 + 0.1917 Age (Y) - 55.1 Height (M) -

0.670 BMI+ 3.470 HGC (CM) + 1.272 FAC (CM)

0

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HIEGHT GROUP WITH (SPEED AND DOMINANCY)

Tall

Meduim

Short

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M: Isotonic Endurance Limit End, 20%-60% l = 29.6 + 0.1917 Age (Y) - 55.1 Height

(M) - 0.670 BMI + 3.470 HGC (CM) + 1.272 FAC (CM)

T: Isotonic Endurance Limit 20%-60% l = 25.0 + 0.1917 Age (Y) - 55.1 Height (M) -

0.670 BMI+ 3.470 HGC (CM) + 1.272 FAC (CM)

BMI effect: According to Sheriff et al. (2012), Montes (2001), Minnal (2014), Al

Meanazel (2013), and Fraser et al. (1999) and Crosby and Wehbe (1994), there is a

positive correlation between physical factors and isotonic endurance limit. Also, Stulen

and De Luca (1981) mentioned that MVC depends on muscles strength and brain-related

factors. Figure 4-26 shows the relationship between isotonic endurance limit and BMI.

Figure 4-26 Relationship between Isotonic Endurance Limit and BMI

Subjects with larger BMI exerted more isotonic endurance limit than other BMI

categories. Highest values were observed in (Isotonic Endurance Limit, 20-60% HS, LH)

condition. The general linear equations for isotonic endurance limit with BMI effect are

as follows:

L: Isotonic Endurance Limit 20%-60% l = 19.9 + 0.1636 Age (Y) - 53.9 Height (M) -

0.256 BMI + 3.341 HGC (CM) + 1.191 FAC (CM)

0

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LS,D LS,ND HS,D HS,ND

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BMI Group (Speed & Dominancy)

Large

Meduim

Small

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M: Isotonic Endurance Limit 20%-60% l = 19.5 + 0.1636 Age (Y) - 53.9 Height (M) -

0.256 BMI + 3.341 HGC (CM) + 1.191 FAC (CM)

S: Isotonic Endurance Limit 20%-60% l = 22.9 + 0.1636 Age (Y) - 53.9 Height (M) -

0.256 BMI + 3.341 HGC (CM) + 1.191 FAC (CM)

Hand Grip Circumference (HGC) effect: Minnal (2014) and Al Meanazel (2013) found

that subjects with higher grip circumference exerted more MVC. Figure 4-27 shows

isotonic endurance limit versus HGC relationships.

Figure 4-27 Relationship between Isotonic Endurance Limit and HGC

Subjects with larger HGC exerted more isotonic endurance limit than other HGC

categories. The highest value is observed in (Isotonic endurance limit, 20-60% HS, LH)

condition. The general linear equations for isotonic endurance limit with HGC effect are

as follows:

0

10

20

30

40

50

60

LS

,D

LS

,ND

HS

,D

HS

,ND

ISO

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HGC GROUP (SPEED & DOMINANCY)

Large

Meduim

Small

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L: Isotonic endurance limit 20-60% l = 23.9 + 0.1641 Age (Y) - 55.6 Height (M) -

0.589 BMI + 3.79 HGC (CM) + 1.140 FAC (CM)

M: Isotonic endurance limit 20-60% l = 23.7 + 0.1641 Age (Y) - 55.6 Height (M) -

0.589 BMI + 3.79 HGC (CM) + 1.140 FAC (CM)

S: Isotonic endurance limit 20-60% l = 24.9 + 0.1641 Age (Y) - 55.6 Height (M) -

0.589 BMI + 3.79 HGC (CM) + 1.140 FAC (CM)

Forearm Circumference (FAC) effect: Anakwe et al. (2007) stated that forearm

circumference generally decreased with age for both men and women, although this

decline was less marked for women. Fraser et al. (1999) also mentioned “that British

subjects have slightly greater values for dominant forearm circumference measurements

in both men and women (29.1) cm Vs (24.3) cm for men and (25.6) cm vs (20.4) cm for

women”. Kallman et al. (1990) found that forearm circumference provides the best

practical measurement for MVC grip strength and muscle mass for both genders. Figure

4-28 shows the relationship between isotonic endurance limit and FAC.

Figure 4-28 Relationship between Isotonic Endurance Limit and FAC

0

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30

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60

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,D

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FAC (SPEED & DOMINANCY)

Large

Meduim

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Table 4-36 Summary of FAC Effect in Isotonic Endurance Limit Test Term Large Medium Small Avg

Isotonic Endurance

Limit, 20-60% LS, RH

40 38.03 37.53 38.52

Isotonic Endurance

Limit, 20-60%, LS, RH

41.5 31.27 32.38 35.05

Isotonic Endurance

Limit, 20-60%, HS, LH

50.57 40.33 40.15 43.68333

Isotonic Endurance

Limit, 20-60%, LS, LH

34.93 28.67 31.26 31.62

Isotonic Endurance

Limit, Avg

41.75 34.575 35.33 37.21833

Subjects with larger FACs exerted more isotonic endurance limits than subjects from

other FAC categories. The highest value was from (Isotonic Endurance Limit, 20-60%

HS, LH) condition. The general linear equations for isotonic endurance limit with age


L: Isotonic Endurance Limit, 20%-60% l = -1.2 + 0.1657 Age (Y) - 55.3 Height (M) -

0.497 BMI

+ 3.437 HGC (CM) + 2.094 FAC (CM)

M: Isotonic Endurance Limit, 20%-60% l = -0.8 + 0.1657 Age (Y) - 55.3 Height (M) -

0.497 BMI

+ 3.437 HGC (CM) + 2.094 FAC (CM)

S: Isotonic Endurance Limit, 20%-60% l = 6.5 + 0.1657 Age (Y) - 55.3 Height (M) -

0.497 BMI

+ 3.437 HGC (CM) + 2.094 FAC (CM)

Trade effect: There was no literature considering the effect of different trades on isotonic

endurance limits. In this dissertation, the trade effect was examined on isotonic endurance

limit for five jobs (APG: Airplane General, E & I: Electrical and Instrument, COMNAV:

Communication and Navigation, Eng: Engine, and GSE: Ground Support Equipment).

Figure 4-28 shows the relationship between isotonic endurance limit and trades.

138

Figure 4-29 Relationship between Isotonic Endurance Limit and Trade for Different

Speeds and Dominancy

Highest isotonic endurance limits were exerted by subjects in engine trade, followed by

subjects in electrical and instrument than those in other trades whereas the lowest isotonic

endurance limits were observed for subjects in ground support equipment trade. The

highest isotonic endurance limit is exerted in (Isotonic Endurance Limit, 20-60% HS,

LH) condition. The general linear equations for isotonic endurance limit with trade effect

are as follows:

APG: Isotonic Endurance Limit, 20-60% = 39.7 + 0.1584 Age (Y) - 53.3 Height (M) -

0.496 BMI + 2.906 HGC (CM) + 1.059 FAC (CM)

COMNAV: Isotonic Endurance Limit, 20-60% = 35.7 + 0.1584 Age (Y) - 53.3 Height

(M) - 0.496 BMI+ 2.906 HGC (CM) + 1.059 FAC (CM)

E&I: Isotonic Endurance Limit, 20-60% = 45.7 + 0.1584 Age (Y) - 53.3 Height (M) -

0.496 BMI+ 2.906 HGC (CM) + 1.059 FAC (CM)

ENG: Isotonic Endurance Limit, 20-60% = 43.9 + 0.1584 Age (Y) - 53.3 Height (M) -

0.496 BMI+ 2.906 HGC (CM) + 1.059 FAC (CM)

0

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ISO

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TRADE (SPEED AND DOMINANCY)

LS,D

LS,ND

HS,D

HS,ND

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GSE: Isotonic Endurance Limit, 20%-60% l = 32.9 + 0.1584 Age (Y) - 53.3 Height (M) -

0.496 BMI

Race effect: There are a very limited number of studies investigating race effect. In this

dissertation, experimental subjects were all Jordanian. Table 4-37 showed the

anthropometric data of the subjects.

Table 4-37 Anthropometric Data Variable Mean StDev Minimum Maximum

Age (Y) 41.712 7.833 25.000 65.000

Weight(Kg ) 82.60 12.85 55.00 114.00

Height (M) 1.7581 0.0705 1.5500 1.9300

BMI 26.679 3.600 18.711 37.422

HGC(CM 22.523 1.338 19.500 25.500

FAC (CM) 29.341 2.441 23.000 35.00

Tables 4-38 and 4-39 showed the general linear and nonlinear models for isotonic

endurance limit.

Table 4-38 General Linear Models for Isotonic Endurance Limit Linear Regression Model Errors

Isotonic Endurance

Limit, 20-60% LS,

Right

ISOTO, END 20-60% LS, RIGHT = 10.447 + 0.21693

AGE (Y) - 0.2437 WEIGHT(KG) - 15.791 HEIGHT (M) +

0.69384 BMI + 1.5815 HGC (CM) + 0.42891 FAC (CM)

RMSE: 15.7

R- SQ: 0.0493

Isotonic Endurance

Limit, 20-60% HS,

RIGHT

ISOTO, END, 20-60% HS, RIGHT= 407.28 + 0.35501

AGE (Y) + 2.1465 WEIGHT(KG) - 266.84 HEIGHT (M) -

7.4043 BMI + 1.3116 HGC (CM) + 2.4362 FAC (CM)

RMSE: 14.1

R- SQ: 0.179

Isotonic Endurance

Limit, 20-60% HS,

LEFT

ISOTO, END, 20-60% HS, LEFT=155.86, - 0.01367,

AGE (Y) + 1.1824, WEIGHT(KG) - 168.78 HEIGHT

(M) - 4.3126, BMI + 7.9354, HGC (CM) + 0.76883,

FAC (CM)

RMSE: 20.7

R- SQ: 0.183

Isotonic Endurance

Limit, 20-60% LS,

LEFT

Isotonic Endurance Limit, 20-60% LS, LEFT = 65.081, +

0.1381 AGE (Y) + 0.1627 WEIGHT(KG) - 67.117

HEIGHT (M) - 1.1717 BMI + 3.3002 HGC (CM) +

0.72653 FAC (CM)

RMSE: 13.6

R- SQ: 0.102

140

Table 4-39 Nonlinear Regression Models for Isotonic Endurance Limit Non Linear equation Errors

Isotonic Endurance

Limit, 20-60% LS,

RIGHT

Isotonic Endurance Limit, 20-60% LS, RIGHT = 40.518 +

0.0025783 AGE (Y) ^2 - 0.00030012 WEIGHT(KG)^2 -

9.9457 HEIGHT(M)^2 + 0.0020933 BMI^2 + 0.034526

HGC(CM)^2 + 0.008021 FAC (CM)^2

RMSE: 15.7

R- SQ: 0.0514

Isotonic Endurance

Limit, 20-60% HS,

RIGHT

Isotonic Endurance Limit, 20-60% HS, RIGHT= 133.28 +

0.0045011 AGE (Y) ^2 + 0.0065015 WEIGHT(KG)^2 -

47.88 HEIGHT(M)^2 - 0.076118 BMI^2 + 0.026819

HGC(CM)^2 + 0.042281 FAC (CM)^2

RMSE: 14

R- SQ: 0.191

Isotonic Endurance

Limit, 20-60% HS,

LEFT

Isotonic Endurance Limit, 20-60% HS, LEFT = 46.61 -

0.00062459 AGE (Y) ^2 + 0.003113 WEIGHT(KG)^2 -

31.131 HEIGHT(M)^2 - 0.041001 BMI^2 + 0.17612

HGC(CM)^2 + 0.013446 FAC (CM)^2

RMSE: 20.6

R- SQ: 0.185

Isotonic Endurance

Limit, 20-60% LS,

LEFT

Isotonic Endurance Limit, 20-60% LS, LEFT= 37.953 +

0.0015277 AGE (Y) ^2 + 0.00015033 WEIGHT(KG)^2 -

15.986 HEIGHT(M)^2 - 0.014257 BMI^2 + 0.071276

HGC(CM)^2 + 0.014364 FAC (CM)^2

RMSE: 13.6

R- SQ: 0.104

This research was the first to examine the isotonic endurance limit for Jordanian subjects

for different speeds and hands including several independent factors.

Smoking effect: Most researchers such as Asano and Branemark (1970), Isaac and Rand

(1969), Davis (1960), and Al Meanazel (2013) found that non-smokers can exert more

force. Isaac and Rand (1969) states that smoking leads to profound vasoconstriction,

results in tissues starving from nutritive blood and bypassing from arterioles to venules.

Figure 4-30 shows the relationship between isotonic endurance limit and smoking.

141

Figure 4-30 Relationship between Isotonic Endurance Limit and Smoking

The effect of smoking on isotonic endurance limit considering (1) low and high speed

and (2) left and right hand is examined. Results show that smokers exerted more isotonic

endurance limit than nonsmokers by a small percentage (1.85%). Due to the experimental

nature, this dissertation concludes that no effect of smoking on highest isotonic

endurance limit, which is again exerted in (Isotonic Endurance Limit, 20-60% HS)

condition. The general linear equations for isotonic endurance limit with smoking effect

are as follows:

NS: Isotonic Endurance Limit, 20-60% l = 29.7 + 0.1589 Age (Y) - 55.7 Height (M) -

0.584 BMI + 3.558 HGC (CM) + 1.138 FAC (CM)

S: Isotonic Endurance Limit 20-60% l = 29.5 + 0.1589 Age (Y) - 55.7 Height (M) -

0.584 BMI

+ 3.558 HGC (CM) + 1.138 FAC (CM)

Hand dominancy effect: Many research studies such as Chatterjee and Chowdhuri

(1991) stated that “isotonic endurance limit for dominant hand is greater 15 Seconds

extra than the non-dominant hand and 16 Seconds more than the non-dominant hand”. Al

0

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EXPERIMENTAL CONDITIONS (SPEED & HAND DOMINANCY)

Smoking

Non Smoking

142

Meanazel (2013) stated that the dominant hand has the highest endurance limit, and hand

dominant strength is affected by age since the dominant hand is used more frequently as a

person ages. The following analysis checks the effect of variable independent factors on

dominancy issue. Figure 4-31 shows the relationship between isotonic endurance limit

and dominancy. There was 122 subjects with the right hand as dominant and 10 subjects

with left hand as dominant.

Figure 4-31 Relationship between Isotonic Endurance Limit and Hand Dominancy

The effect of hand dominancy on isotonic endurance limit considering (1) low and high

speed, and (2) left and right hand is examined. Results show that there is almost no effect

of dominancy on isotonic endurance limit. Highest values of isotonic endurance limit are

exerted in (Isoto, End, 20-60% HS, LH) condition. Note that highest values of isotonic

endurance limit are exerted by subjects aged above 50 years old and isotonic endurance

limit decreases as the age of the subject decreases. This finding confirmed some studies

in the literature. For example, Chatterjee and Chowdhuri (1991) found that the dominant

31

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min

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Dominancy Effect)

Isoto,End,20%-60% lS

143

hand sustained extra isotonic endurance limit (on average 16 seconds) more than the non-

dominant hand. Sorensen et al. (2009) found “endurance of dominant hand (is) 15

seconds more than the non-dominant hand”. Al Meanazel (2013) observed that the

dominant hand has the highest endurance limit. The general linear equations for isotonic

endurance limit with hand dominancy effect are as follows:

Dominant and Non-dominant Hand

D: Isotonic Endurance Limit, 20%-60% l = 30.0 + 0.1571 Age (Y) - 55.8 Height (M) -

0.584 BMI + 3.542 HGC (CM) + 1.145 FAC (CM)

ND: Isotonic Endurance Limit, 20%-60% l = 30.9 + 0.1571 Age (Y) - 55.8 Height (M) -

0.584 BMI + 3.542 HGC (CM) + 1.145 FAC (CM)

Right and Left Hand

L: Isotonic Endurance Limit, 20%-60% l = 29.8 + 0.1595 Age (Y) - 55.6 Height (M) -

0.579 BMI+ 3.558 HGC (CM) + 1.129 FAC (CM)

R: Isotonic Endurance Limit, 20%-60% l = 29.2 + 0.1595 Age (Y) - 55.6 Height (M) -

0.579 BMI + 3.558 HGC (CM) + 1.129 FAC (CM)

4.8 MODELING WITH NEURAL NETWORK

Both neural network coding and toolbox in Matlab 15 calculate the maximum voluntary

contraction (MVC) for the following outputs:

1. MVC

2. MVC (Kg, Sit, D)

3. MVC (Kg, Sit, ND)

4. MVC (Kg, Stand, D)

5. MVC (Kg, Stand, ND)

6. Isometric Endurance Limit (20%)


144



10. Isometric Endurance Limit (Avg)

11. Isotonic Endurance Limit 20-60% Low, S, Right

12. Isotonic Endurance Limit 20-60% High, SP, Right

13. Isotonic Endurance Limit 20-60% High, SP, Left

14. Isotonic Endurance Limit 20-60% Low, SP, Left

15. Isotonic Endurance Limit 20-60% (Avg)

The following continuous inputs were used:

1. X1: Age (Y)

2. X2: Height (M)

3. X3: BMI

4. X4: HGC (CM)

5. X5: FAC (CM)

The experiment assumptions are as follows:

1- Training set: 70% or 358 samples where the neural network was adjusted and

attuned according to its error.

2- Validation set: 15% or 79 samples; to find and measure neural network

generalization, and to stop the training process when generalization achieves the

highest accuracy and the process stops improving.

3- Testing set: 15% or 79 samples, as an independent measure of neural network

performance.

4- Number of hidden neurons: 10 neurons.

5- General learning algorithm used is backpropagation since it is an effective

algorithm to adjust the weight on each node created by data. Input training set was

chosen similar to Heaton (2005). It is generally used when there is a large amount

145

of input/output and the relationship between those inputs and outputs is complex or

unknown.

6- Training algorithem used is Levenberg–Marquardt where it takes less time using

more memory and stops when generlization achieves the most performance as

indicated by increase in mean square error. Backpropagation could be used as well.

Similar to Beale et al. (1998), Levenberg-Marquardt algorithm was selcted due to

its fast adjustment mechanisms.

7- The experiment information used in a feed-forward neural network is transferred in

only one direction; that is, it moves from the input layer through the hidden layer

and then to the output layer.

8- Hidden layer may contain one or more hidden layers .

9- Validation checks: 6

Figure 4-31 shows a general diagram of the three layers of nodes in a neural network.

Mean square errors and R value are shown in Tables 4-40, and Table 4-41, 4-42, 4-43 for

the three tests: MVC, isometric and isotonic endurance limits. Theses values are small

implying that neural network achieved good performance.

Table 4-40 Summary of Neural Network Performance (MVC, Isometric and

Isotonic Endurance Limits)

MVC Isometric Endurance

Limit

Isotonic Endurance Limit

MSE R MSE R MSE R

7.09 e -8 9.9 e-1 3.35 e-7 9.9 e-1 1.2 e-3 9.9 e-1

1.56 e-7 9.9 e-1 3.4 e-7 9.9 e-1 6.5 e-4 9.9 e-1

7.51 e-8 9.9 e-1

2.54 e-7 9.9 e-1

2.4 e-3 9.9 e-1

146

Figure 4-32 General Neural Network Diagram

Table 4-41 Neural Network Performance for MVC Test

MVC

MSE R-Sq

7.09 e -8 9.9 e-1

1.56 e-7 9.9 e-1

7.51 e-8 9.9 e-1

Table 4-42 Neural Network Performance for Isometric Endurance Limit


MSE R-Sq

3.35 e-7 9.9 e-1

3.4 e-7 9.9 e-1

2.54 e-7 9.9 e-1

147

Table 4-43 Neural Network Performance for Isotonic Endurance Limit


MSE R-Sq

1.2 e-3 9.9 e-1

6.5 e-4 9.9 e-1

2.4 e-3 9.9 e-1

Neural network performance on isotonic endurance limit for the training set is shown in

Table 4-46, where experiment samples are divided into three parts (training, validation

and testing). First, the training data set is used to build the neural network. Neural

network training continues given that the neural network continues improving while

checking with the validation set. The neural network training stopping point is

highlighted in green. Neural network performance for the three tests is shown in Table 4-

46. It shows the neural network performance improvement during the training process. In

neural networks, the performance is calculated in terms of mean squared error (MSE; Y

axis log scale). MSE rapidly decreased as the network was developed and trained. Table

4-4 shows that the best validation performance was at 1.5 e-7 at epoch 554 for MVC and

3.41 e-7 at epoch 1000 for isometric endurance limit and .0000655 at epoch 16 for the

test of isotonic endurance limit. In this research, all results are reasonable since the final

MSEs are very small. The testing and validations errors are similar, and no significant

over fitting has occurred.

148

Table 4-44 Neural Network Performance for the Three Tests

MVC

Isometric

Endurance

Limit

Isotonic

Endurance

Limit

149

Other neural network performance measure includes the error histogram. Table 4-45

shows the error size distribution. Most errors are near zero, as viewed for the three tests

(MVC, isometric and isotonic endurance limits).

Table 4-45 Neural Network Error Histogram

MVC

Isometric

End Limit

150

Isotonic

Endurance

Limit

Neural network function fit plot is shown in Table 4-46. Besides, it plots the experiment

targets. The error bars show the difference between inputs and outputs which is very little

for all neural network model of MVC, isometric and isotonic endurance limits.

151

Table 4-46 Neural Network Function Fit Plot

MVC

Isometric

Endurance

Limit

152

Isotonic

Endurance

Limit

4.9 Neural Network Regression

Other ways of measuring performance of neural network (i.e., how neural network fits

the data) include the regression plots. In the dissertation, the regression plots are

generated for the three tests. It plots the neural network outputs against experiment target

values. Table 4-47 shows that the neural network models have learned and fitted the

experiment data well. Outputs match the experiment targets accurately for the three

datasets (training , testing , and validation) sets. R values equal 1.

153

Table 4-47 Neural Network Regression Plots for the Three Tests

MVC

Isometric

Endurance

Limit

154

Isotonic

Endurance

Limit

4.9 ANFIS Analysis

ANFIS analysis is performed in this section. Table 4-48 and 4-49 shows those overall

ANFIS output errors and those for each experimental condition, respectively. By

examining the output over the whole training period, it is clear that the experimental

checking dataset obtains minimum checking error. Also, step-size errors show very small

numbers which serves to adjust references for the initial step-size, and increasing and

decreasing rates. In general, the checking error should decrease until the training assigned

point, and then increases. This point is called model over fitting point. Detailed ANFIS

info is as follows:

o Number of nodes: 1016

o Number of linear parameters: 2916

o Number of nonlinear parameters: 54

o Total number of parameters: 2970

o Number of training data pairs: 100

155

o Number of checking data pairs: 0

o Number of fuzzy rules: 486

o Epoch completed at: 49, 50

Table 4-48 ANFIS Output Errors for the Three Tests (MVC, Isometric and Isotonic

Endurance Limits)

Test Results Error

MVC 3.73432 Step size (0.005905)

Isometric Endurance Limits 4.2323e-05 Step size (0.008100)

Isotonic Endurance Limits 3.6203e-05 (0.006561)

Table 4-49 ANFIS Output Errors for Each Experimental Condition

Test Results Error

MVC (Kg, Sit, D) 3.84522e-05

MVC (Kg, Sit, ND) 2.46537e-05

MVC (Kg, Stand, D) 3.6203e-05

MVC (Kg, Stand, ND) 1.56111e-05

Isometric Endurance Limit (20%) 0.000128428

Isometric Endurance Limit (40%) 5.33146e-05



Isotonic Endurance Limit, 20-60% low,

SP, RH

3.00345e-05

Isotonic Endurance Limit, 20-60% High,

SP, RH

1.73763e-05

Isotonic Endurance Limit, 20-60% High,

SP, LH

4.6505e-05

Isotonic Endurance Limit, 20-60% low,

SP, LH

4.61178e-05

156

Figure 4-33 ANFIS Diagram

157

CHAPTER FIVE CONCLUSIONS AND FUTURE WORK

5.1 CONCLUSION ON MATHEMATICAL MODELING

Experimental studies were conducted with a psychophysical approach to examine the

effect of static/dynamic forces, on the hand grip fatigue and strength, maximum voluntary

contraction (MVC), fatigue limits, and endurance for subjects in the aviation industry. In

this comprehensive research, nine independent factors were considered which are most

likely to represent all possible factors considered by other researchers during the last 60

years. To fill a significant literature gap, several new factors had been investigated for

their effects on MVC and hand muscle fatigue, including new apparatus (digital

dynamometer), hand volume, forearm grip circumference, new race (Jordanian subjects),

new posture (standing and sitting), large smoker sample, and middle-age to older (from

25 to 55 years old) subjects. The uniqueness and significance of the research was

illustrated in the application to engineers and different trade’s mechanics in the aviation

industry, where a combination of isometric and dynamic isotonic forces is applied in

performing tasks. Whereas the results from this dissertation verify other researchers'

work, it also proposes comprehensive models considering nine different factors. Finally,

this research could be considered as a standard procedure for comparisons and

conducting future research. Results were analyzed by many statistical test, mathematical

modeling and machine learning techniques. General, detailed, and precise models

(mathematical and Artificial Neural Network and ANFIS models) were developed to

predict MVC, maximum isometric endurance limit of submaximal (20%, 40%, 60% and

158

80%) of MVC, and isotonic fatigue endurance between 20% and 60% of MVC. The

experimental results were presented in three sections, and each section were analyzed in

the following manner: Part (1) focuses on maximum voluntary contraction (MVC), Part

(2) is devoted to isometric muscle fatigue limit for different MVC ratios (20%, 40%, 60%

and 80%), and Part (3) studies isotonic muscle fatigue for between 20% and 60% of the

MVC force. Each part considers outputs of four special cases and nine independent

factors (between 2-6 levels) as shown in Table 5-1 with a total of 29 levels. In contrast to

many studies in the literature, this dissertation considers all factors which might have a

significant effect. Literature review showed that most of other researchers reported their

findings with simple comparisons. This dissertation also reported descriptive statistics for

comparisons with other researchers' findings. Both linear and nonlinear modeling for

each independent factor was performed. All independent factors had correlation effects as

expected, since most of them are related to subjects' physical factors (of the human body)

such as forearm, hand grip circumference, height, weight, and body mass index. The

correlation effect appeared only as negative between MVC and isotonic endurance limit

(low & high speed) and between age and height for experimental subjects, and positive

between MVC values and isometric endurance limit at 20% of the MVC, and between

isometric endurance limit and isotonic endurance limit. Experiment data for MVC and

Isometric endurance limit followed normal distributions. Box-cox transformation was

used for isotonic endurance limits. Also, all potential outliers had been investigated for

validity. Subject group ages from 25 to 60 years old, with the following basic statistic:

age (41.71 years old), weight (82.6 Kg), height (1.75 m), BMI (26.67), hand grip

circumference (22.52 cm), and forearm circumference (29.34 cm). Detailed data are

159

provided in the Appendices for all ages and trades. Subjects from the electrical and

instrument trade are the youngest and heaviest among all trades, with height, HGC and

FAC being almost the same as all other trades. The summary of MANOVA results

included mostly all independent factors: (1) age, height, trade, forearm circumference

(FAC), hand grip circumference (HGC) and BMI for MVC; (2) trade, forearm

circumference (FAC), hand grip circumference (HGC) for isometric endurance limit; and

(3) age, height, trade, forearm circumference (FAC) and hand grip circumference (HGC)

for isotonic endurance limit. In this study, ANOVA was conducted with full factorial

experimental design on the following factors: age (6 levels: A0, A1, A2, A3, A4, A5),

trade (5 levels: COMNAV, ENG, GSE, APG, E&I), height (3 levels: short, medium, tall),

BMI (3 levels: large, medium, and small), hand grip circumference (HGC; 3 levels: large,

medium, and small), forearm grip circumference (FAC; 3 levels: large, medium, and

small), dominancy (2 levels: dominant and non-dominant), and posture (2 levels: sitting

and standing). MANOVA/ANOVA tests verify all independent factors as significant

factors. Residual plots show that the model fit in ANOVA and regression analysis is

satisfactory. The normal probability plot of residuals shows that the normality assumption

holds, since it is forming a straight line with few points that depart from the straight line.

The plot of residuals versus fitted values tests the constant variance assumption and

shows the pattern (random) of the experiment residuals on both sides of the graph, with

no data points far away from the majority of points, i.e., outliers. The histogram of the

residuals shows the general characteristics of experimental data and plots the residuals

that include typical values, spread and shape. The plot shows no skewed distribution. The

plot of residuals versus order of data shows a correlation between experimental factors

160

and collected data. The plots of both main effects and interactions confirm results from

the ANOVA regarding significant factors. Table 5-1 shows the General Linear and

Nonlinear Models for MVC Test.

Table 5-1 General Linear and Nonlinear Models for MVC Test (MATLAB 15)

Linear

Model

MVC= -21.594 -0.43487 AGE(Y) + 22.073 HEIGHT

(M) -0.36207 + BMI 0.14221 HGC (CM) +

1.8439 FAC (CM)

RMSE: 6.31

R-Sq: 0.448,

R-Sq,(Adj) 0.443

Non

Linear

Model

MVC= 13.786 + -0.0051191 * AGE(Y)^2 +

6.0779*HEIGHT(M)^2 -0.006859 *BMI^2 +

0.0028544*HGC(CM)^2 + 0.030977*FAC (CM)^2

RMSE: 6.3

R-Sq: 0.451,

R-Sq,(Adj) 0.445

Detailed general linear and nonlinear models and stepwise models for the three tests

(Maximum Voluntary Contraction, Isometric Endurance Limit and Isotonic Endurance

Limit) are obtained for the following independent factors: Age (6 levels), Trade (5

levels), Height (3 levels), BMI (3 levels), Hand grip circumference (HGC; 3 levels),

Forearm grip circumference (FAC (3 levels), Dominancy (2 levels), and Posture (2

levels). These detailed models establish a baseline for future studies and will be easier for

comparisons. Tables 5-2 to 5-11 shows the detailed independent factors effect with

conclusion for MVC

Table 5-2 Posture Effect on MVC Factor Findings Conclusion

posture

(standing,

sitting)

Standing Posture Avg: 46.6 Kg

Sitting Posture Avg : 46.255 KG

Standing/Sitting (overall) Percentage

extra with .07%

1. Aviation industry subjects exerted

almost same MVC in both postures

2. Highest MVC value was in (30-

<35) age group followed by A0:

(25-<30) age group

3. Lowest MVC value in older ages

(above 50 year old)

161

Table 5-3 Age Effect on MVC Factor Findings Conclusion

Age MVC (KG) (SIT, D)

Highest MVC: A3 (40-<45) : 49.73

Lowest MVC: A5 (above 50): 39.18

Same MVC: A3 (40-<45), A1 (30-

<35), A2 (35-<40)

MVC (KG) (SIT, ND)

Highest MVC: A0 (25-<30): 50.76


Same MVC: A0 (25-30), A1(30- <35)

MVC (KG) (STAND, D)

Highest MVC: A3(40-<45): 51.43


Same MVC: A3 (40-<45), A1 (30-

<35)

MVC (KG) (STAND, ND)

Highest MVC: A1(30- <35): 49.04

Lowest MVC: A5(above 50): 36.99 Same MVC: A1(30-<35),A0(25-<30)

1- Aviation industry subjects exerted

different MVC for different age

groups

2- Highest MVC value was in (30-

<35) age group followed by A0:

(25-<30) age group

3- Lowest MVC value in older ages

(above 50 years old)

Table 5-4 Height effect on MVC Factor Findings Conclusion

Height MVC (Kg,Sit,D) T (53.58), M(7.080 ),

S(42.21),

MVC (Kg,Sit,ND) T (49.59), M (46.633), S

(41.41)

MVC (Kg,Stand,D) T (54.59), M48.430 ), S

(44.23)

MVC (Kg,Stand,ND) T (47.75), M (46.090), S

(40.36)

1. Height has a major effect

on MVC

2. Taller people exerted

more MVC than medium

(9.1%) and shorter

(12.21%)

162

Table 5-5 BMI Effect on MVC Factor Findings Conclusion

BMI MVC (Kg, Sit, D) L(47.75), M( 47.78),

S(46.35)

MVC (Kg, Sit, ND) L( 45.88), M (46.831),

S (44.90)

MVC (Kg, Stand, D) L(48.22), M (49.72),

S (47.74 )

MVC (Kg, Stand, ND) L(46.03), M(45.94),

S (43.24 9.07)

1. BMI has a minor effect on

MVC

2. Medium BMI subjects exerted

higher MVC than large BMI

subjects (by 1.2%) and small

BMI subjects (by 4.43%)

3. Highest MVC exerted in

MVC (Kg, Stand, D)

condition.

Table 5-6 Hand Grip Circumference (HGC) Effect on MVC

Factor Findings Conclusion

Height MVC (Kg,Sit,D) LARGE

(52.05),MEDIUM (46.56),SMALL

(44.23)

MVC (Kg,Sit,ND) LARGE (50.04),

MEDIUM (45.61), SMALL (42.84)

MVC (Kg,Stand,D) LARGE

(53.26), MEDIUM (48.27), SMALL

(45.38),

MVC (Kg,Stand,ND) LARGE

(49.77), MEDIUM (44.47), SMALL

(41.62)

1. HGC has a major effect on MVC

2. Subjects exerted more MVC when

they have larger FGC

3. Highest MVC exerted in MVC (Kg,

Stand, D) condition

Table 5-7 Forearm Circumference (HGC) Effect on MVC


Height MVC (Kg, Sit,D, LARGE(53.48),

MEDIUM(46.41), SMALL(43.85)

MVC (Kg,Sit,ND, LARGE(51.53),


MVC (Kg,Stand,D), LARGE(55.51),

MEDIUM(48.07), SMALL(44.44),

MVC(Kg,Stand,ND) LARGE(52.89),


1. FAC has a major effect on

MVC

2. Subjects exerted more MVC

when they have larger FGC

3. Highest MVC exerted in

MVC (Kg, Stand, D)

condition

163

Table 5-8 Trade Effect on MVC


Height MVC (Kg,Sit,D), APG (47.03), COMNAV

(47.27), E&I (48.02), ENG (47.45) ,GSE(47.14)

MVC (Kg,Sit,ND), APG (46.06 ), COMNAV

(45.10), E&I (44.58), ENG (46.00), GSE (46.8)

MVC (Kg,Stand,D), APG (48.31), COMNAV

(48), E&I (49.77), ENG (49.15), GSE (49.15)

MVC (Kg,Stand,ND) APG (44.94), COMNAV

(42.99), E&I (46), ENG (45.88), GSE (44.33)

1. Trade has a minor effect

on MVC (All trades

mostly exerted the same

MVC)

2. Highest MVC exerted

by engineers and E& I

trades

3. Consider mean age for

trades and smoking

status.

4. Engineer and E& I have

the mean ages 42 and

37, respectively

Table 5-9 Race Effect on MVC Population MVC (Kg)

(Male)

MVC (Kg)

(Female)

Author(s) (Year)

Singaporean 24.1 N/A Incel et al. (2002)

Indian 30-39.8 22.75 Vaz et al. (1998, 2002), Koley et al.

(2009)

Jordan (Pilot Study) 33.619 N/A Al-Momani (2015)

Spanish 39.95 25.72 Heredia et al. (2005)

Scotland 35.12 23.02 Heredia et al. (2005)

Scotland 40.0–48.8 27.5–34.4 Brenner et al. (1989)

Jordan 46.58167 N/A Al-momani (2015)

USA 62.0 37.0 Crosby & Wehbe (1994)

USA 44.8 35.0 Al Meanazel (2013)

Heredia et al. (2005) found that Jordanian subjects exerted higher MVC than

Singaporean, Indian, Spanish and Scotland subjects, and less than UK and USA subjects;

however, this result cannot be considered conclusive since each experiment has its

environment and different subjects. The research considers race factor as an important

164

factor since it is related to culture, lifestyle, and physical factors of human races in

general. Table 5-10 Smoking Effect on MVC


Smokers MVC (Kg,Sit,D), S(47.59)

MVC (Kg,Sit,ND), S(46.52)

MVC (Kg,Stand,D), S(49.52)

MVC (Kg,Stand,ND), S(45.39)

1. Smokers exerted more MVC than

non-smokers by 2%

2. Difference is not high

3. Highest MVC was exerted in

MVC (Kg, Stand, D) condition

Non

smokers MVC (Kg,Sit,D), NS(46.822)

MVC (Kg,Sit,ND), NS(45.53)

MVC (Kg,Stand,D), NS(47.99)

MVC (Kg,Stand,ND), NS(44.44)

Table 5-11 Dominancy Effect on MVC Factor Findings Conclusion

Dominancy

(standing, sitting)

Standing

Dominant=48.26kg

Standing non

Dominant:44.93 kg

Sitting Dominant: 46.58kg

Sitting non dominant:45.93

kg

1. Dominant hand exerted more MVC

by 7.41%,

2. Non-dominant hand exerted more

MVC by 1.41%

3. The highest MVC was exerted by the

dominant hand of subjected aged 30-

45 years old, followed by those 25-30

years old; the MVC decreased for

subjects above 45 years old

5. Non-dominant hand of the younger

subjects aged 25-30 years old exerted

more MVC, followed by 30-35 years

old; the MVC decreased above age 35

years old .

165

Tables 5-12 through 5-32 shows the independent factors effect and detailed conclusions

for Isometric fatigue limits)

Table 5-12 General Linear Models for Isometric Endurance Limit Linear Regression Model Errors


(20%)

127.25 + -0.0014601 * AGE(Y)^2 + -

37.427 *HEIGHT(M)^2 + -

0.05763*BMI^2 +

0.084164*HGC(CM)^2 + 0.18183*FAC

(CM)^2

RMSE: 58.4

R-Sq: 0.116

R-Sq,(Adj)

0.107


(40%)

177.76 -0.0072556* AGE(Y)^2 -

18.581*HEIGHT(M)^2 -

0.035377*BMI^2 -

0.16351*HGC(CM)^2 + 0.086309*FAC

(CM)^2

RMSE: 33.5

R-Sq: 0.117

R-Sq,(Adj)

0.109


(60%)

83.723-0.0016898 b2* AGE(Y)^2 -

8.1407*HEIGHT(M)^2 -

0.024757*BMI^2 -

0.088919*HGC(CM)^2 + 0.05318*FAC

(CM)^2

RMSE: 21

R-Sq: 0.0835,

R-Sq,(Adj)

0.0747


(80%)

44.498 + 0.0016698* AGE(Y)^2 -

2.8629*HEIGHT(M)^2 -

0.011467*BMI^2 -

0.072948*HGC(CM)^2 +

0.032936*FAC (CM)^2

RMSE: 13.1

R-Sq: 0.0891,

R-Sq,(Adj)

0.0804


(Avg)

108.31 + -0.0021839* AGE(Y)^2 + -

16.753*HEIGHT(M)^2 + -

0.032308*BMI^2 + -0.060302

*HGC(CM)^2 + 0.088564*FAC

(CM)^2

RMSE: 23.8

R-Sq: 0.109

R-Sq,(Adj)

0.101

166

Table 5-13 Isometric Endurance Limit Non Linear Regression Non- Linear Regression Model Errors

Isometric

Endurance

Limit (20%)

127.25 + -0.0014601 * AGE(Y)^2 + -37.427

*HEIGHT(M)^2 + -0.05763*BMI^2 +

0.084164*HGC(CM)^2 + 0.18183*FAC

(CM)^2

RMSE: 58.4

R-Sq: 0.116

R-Sq,(Adj) 0.107

Isometric

Endurance

Limit (40%)

177.76 -0.0072556* AGE(Y)^2 -

18.581*HEIGHT(M)^2 -0.035377*BMI^2 -

0.16351*HGC(CM)^2 + 0.086309*FAC

(CM)^2

RMSE: 33.5

R-Sq: 0.117

R-Sq,(Adj) 0.109

Isometric

Endurance

Limit (60%)

83.723-0.0016898 b2* AGE(Y)^2 -

8.1407*HEIGHT(M)^2 -0.024757*BMI^2 -

0.088919*HGC(CM)^2 + 0.05318*FAC

(CM)^2

RMSE: 21

R-Sq: 0.0835,

R-Sq,(Adj) 0.0747

Isometric

Endurance

Limit (80%)

44.498 + 0.0016698* AGE(Y)^2 -

2.8629*HEIGHT(M)^2 -0.011467*BMI^2 -

0.072948*HGC(CM)^2 + 0.032936*FAC

(CM)^2

RMSE: 13.1

R-Sq: 0.0891,

R-Sq,(Adj) 0.0804

Isometric

Endurance

Limit (Avg)

108.31 + -0.0021839* AGE(Y)^2 + -

16.753*HEIGHT(M)^2 + -0.032308*BMI^2 + -

0.060302 *HGC(CM)^2 + 0.088564*FAC

(CM)^2

RMSE: 23.8

R-Sq: 0.109

R-Sq,(Adj) 0.101

167

Table 5-14 Age Effect on Isometric Endurance Limit

actor Findings Conclusion

Age 1. Isometric Endurance Limit (20%),

A0: (25-<30) (159.2),A1: (30- <35),(176.4),A3:

(40-<45),(159.73),A2: (35-<40)(163.22)

A4: (45-<50)(177.3),A5 (above 50)(158.9)


A0: (25-<30) (111.1),A1: (30- 35)(86.91),A3: (40

45)(61.27),A2: (35-<40)(76.52),A4:(45-

<50)(68.07),A5 (above 50)(75.67)


A0: (25-<30)(45.56),A1: (30- <35)(49.45),A3:

(40-45)(32.79),A2: (35-<40)(42.07),A4: (45-

<50)(34.6),A5 (above 50)(45)


A0: (25-<30) (24.22),A1: (30- <35) (29.3),A3:

(40-<45)(19.05) A2: (35-<40)(21.52),A4: (45-

<50) (19.65),A0: (25-<30) (30.11)

1. Highest isometric

mean endurance limit

exerted in A0: (25-

<30) followed by

30-35 years ago

group and then starts

decreasing by older

ages


mean endurance limit

(85 Sec) and then

start decreasing by

older ages.

3. Aviation industry

subjects exerted high

endurance on low

MVC percentages

than high percentages

Table 5-15 Height effect on Isometric Endurance limit


Height Isometric Endurance Limit (20%)

Tall (171),Medium (172.93),Short (153.29)

Isometric Endurance Limit (40%)

Tall (70.76),Medium (72.72),Short (75.94)





1. Limited effect of height

on isometric endurance

limit.

2. Subjects with medium to

tall height exerted higher

endurance limits


endurance limit exerted

in (20%) condition.

168

Table 5-16 BMI Effect on Isometric Endurance Limit



Large (174.9),Medium (166.1),Small

(168.67)


Large (59.12),Medium (73.88), Small

(79.79)



(40.23)



(21.27)

1.Limited effect of BMI on

isometric endurance limit

2.Subjects with medium to small

BMI exerted higher endurance

limits average by 8.83% than

those with large BMI

3.Subjects with large BMI values

have lowest isometric endurance

limit.

4. Highest Isometric endurance

limit exerted in (20%) condition

Table 5-17 Hand Grip Circumference (HGC) Effect on Isometric Endurance Limit



Large (185),Medium (168.66),Small

(149.037)



(75.15)



(37.74)



(23.05)

1. Limited effect of HGC on


2. Subjects with medium HGC

have higher endurance limits

average by 4.84% than those

with large HGC and 9.12 than

those with small HGC

3. Small HGC have the weakest


4. Highest isometric endurance

limit exerted in (20%)

condition

5. Final conclusion: subjects with

larger HGC can exerted larger


169

Table 5-18 Forearm Grip Circumference (FGC) Effect on Isometric Endurance

Limit



Large (190.8), Medium

(165.79),Small (151.6)


Large (74.14), Medium (71.99), Small

(74.62)



(38.76)


Large (24.14), Medium (21), Small

(21.32)

1. Subjects with larger FGC

exerted more isometric

endurance limit in all

percentages followed medium

and smaller FAC subjects.

2. Subjects with larger FGC can

exerted larger isometric

endurance limit

Table 5-19 TRADE Effect on Isometric Endurance Limit



APG (160.62),COMNAV (138.3 ),E&I

(166.2) ,ENG (200.2) ,GSE(129.7)


APG(87.06 ) , COMNAV(53.88) ,E&I

(56.7),ENG (65.69),GSE (71.80)


APG (45.62),COMNAV (28.5),E&I

(29),ENG (34.4),GSE (38.13)


APG(16.86 ),COMNAV (11.2),E&I

(15.25),ENG (8.1) ,GSE (10.11)

1. Trade has a major effect on



limit exerted by APG and

Engine trades then E& I

3. Lowest isometric endurance

limit exerted in COMNAV

170

Table 5-20 Isometric Endurance Limit for Jordanian Subjects Variable Mean StDev Minimum Maximum

Isometric End, Limit (20%) 167.45 3836.74 60.00 343.00




Table 5-21 Smoking effect on Isometric End, Limit Factor Findings Conclusion

Smokers Isometric End, Limit (20%) S(176.31)

Isometric End, Limit (40%) S(78.05)



1. Smokers exerted more

isometric endurance limit than

non-smokers by 12.98%

2. Difference is not high

3. Highest exerted in isometric

end, limit (20%).

4. Reason: Nature of experiment

(low to medium effort) and

56% smokers and younger ages

Non

smokers

Isometric End, Limit (20%) NS(156.14)



Isometric End, Limit (80%) NS(21.34

Tables 5-22 through 5-32 shows the independent factors effect and detailed conclusions

for each case (MVC, Isometric fatigue limits and Isotonic fatigue limits)

171

Table 5-22 Hand Dominancy Effect on Isometric End, Limit


Dominant Isometric End, Limit (20%), (166.39)

Isometric End, Limit (40%), (74.01)

Isometric End, Limit (60%) ,(38.85)

Isometric End, Limit (80%) ,22.23)

1. Dominant hand exerted

more isometric endurance

mit by 3.57%

2. The highest isometric

endurance limit exerted for

dominant hand for age

group A4:(45-<50)

followed by A2:(35-<40)

3. Lowest isometric end, limit

exerted in A5 (above 50)

and A0 and A0: (25-<30)

Non

Dominant

IsometricEnd, Limit (20%), (180.4)




Table 5-23 Isotonic Endurance Limit General Linear and Nonlinear Models

Model Model Summary

Linear

Model

29.495 + 0.15947AGE(Y) -55.559 EIGHT

(M) -0.57916 BMI + 3.558 GC (CM) +

1.1294 FAC (CM)

RMSE: 16.9

R-Sq: 0.0914,

R-Sq,(Adj) 0.0827

Non

Linear

Model

33.635 + 0.0018137* AGE(Y)^2 + -

16.255*HEIGHT(M)^2 + -0.010432

*BMI^2 + 0.077773*HGC(CM)^2 +

0.0204*FAC (CM)^2

RMSE: 16.9

R-Sq: 0.0939,

R-Sq,(Adj) 0.0852

172

Table 5-24 Age Effect on Isotonic Endurance limit


Age 1.Isoto, End 20-60%,S, RH, A0: (25-<30)

(31.67),A1:(30- 35),(37.55),A3:(40-

<45),(38.88),A2:(35-<40)(36.63)A4:(45-

<50)(38.47),A5(above 50)(48.22)

2.Isoto, End, 20-60%,S,RH , A0: (25-<30)

(27.22),A1:(30- 35)(39.45),A3: (40-

45)(28.3),A2: (35-<40)(32.85),A4: (45-

<50)(35.56),A5 (above 50)(47)

3.Isoto, End 20-60%,HS, LH A0: (25-

<30)(28.89),A1: (30- <35)(48.73),A3: (40-

45)(43.09),A2: (35-<40)(37.3),A4: (45-

<50)(46.58),A5 (above 50)(40.78).

4.Isoto, End 20-60% LS, LH A0: (25-<30)

(36.64),A1: (30- <35) (28.24),A3: (40-

<45)(27.63) A2: (35-<40)(33.16),A4:(45-

<50) (36.22),A0: (25-<30), (30.11)

1- Older subjects aged

above 50 exerted highest

isotonic endurance limit,

followed by subjects aged

between 45 and 50 years

old

2. Subjects of youngest

ages have the lowest

isotonic endurance limit.

3. Highest isotonic

endurance limit exerted in

(Isoto, End 20-60% LS,

RH) condition.

Table 5-25 Height Effect on Isometric Endurance Limit Factor Findings Conclusion

Height Isoto, End 20-60 %( LS, RH)


Isoto, End, 20-60 %( HS,RH)

Tall (31.55),Medium (34.01),Short (35)

Isoto, End, 20-60 %( HS, LH )


Isoto, End 20-60 %( LS, LH)


1. Limited effect of height on

isometric endurance limit.

2. Subjects of medium height

exerted higher endurance limits


limit was exerted in (20%)

condition.

Table 5-26 BMI Effect on Isometric Endurance Limit



Large (43.35),Medium (36.62),Small

(37.65)

Isoto, End, 20-60 %( HS, RH)


(33.29)

1.Limited effect of BMI on

isometric endurance limit.

2.Subjects with larger BMI

exerted higher isotonic

endurance limits.


173

Isoto, End, 20-60 %( HS, LH)


(42.33)



(36.13)

endurance limit was exerted

in Isoto, End, 20-

60%(HS,LH) condition.

Table 5-27 Hand Grip Circumference (HGC) Effect on Isom, End, and Limit



Large (40.45), Medium (38.31),Small

(36.38)



(30.68)



(33.55)



(25.38)

1. Larger HGC subjects exerted

more isotonic endurance limit

than subjects with other HGCs

2. Highest isotonic endurance limit

was exerted in Isoto, End, 20-60

%(HS, LH)

Table 5-28 Forearm Effect on Isometric Endurance limit



Large (40),Medium (38.031),Small (37.53)


Large (41.5),Medium (31.27), Small (32.38)



(40.15)



(31.26)

1. Larger FAC subjects

exerted more isotonic

endurance limit than

subjects with other FACs

2. Highest isotonic endurance

limit is exerted in Isoto,

End, 20-60 %( HS, LH)

174

Table 5-29 Trade Effect on Isometric Endurance Limit

Facto

r

Findings Conclusion

Heigh

t Isoto, End 20-60 %( LS, RH)

APG (39.68),COMNAV (37.19),E&I (41) ,ENG

(39.17) ,GSE (31.27)


APG(34.09 ) , COMNAV (26.56)

,E&I(29.17),ENG (38.1),GSE (29.67)


APG (36),COMNAV (35.81),E&I (46.83),ENG

(57.02),GSE (29.8)


APG(28.74 ),COMNAV(23.19),E&I(38.3),ENG

(36.29) ,GSE(26.67)

1. Trade has a major effect

on isometric endurance

limit

2. Highest on isometric

endurance limit was

exerted by Engine than

Electrical& Instrument

trade


endurance limit exerted

Isoto, End, 20-60 %(

HS, LH) condition

4. Engine and E& I have

the mean ages of 42 and

37, respectively

Table 5-30 Isometric Endurance Limit for Jordanian Subjects


Isoto, End 20-60 %(

LS, RH)

38.32 15.72 6.00 110.00


HS, RH)

33.73 15.18 9.00 80.00


HS, LH)

42.45 22.33 9.00 109.00

Isoto, End 20-60 %(

LS, LH)

30.67 13.99 7.00 85.00

175

Table 5-31 Smoking Effect on Isometric Endurance Limit


Smokers Isoto, End 20-60 %( LS,

RH)S(37.91)

Isoto, End, 20-60 %( HS,

RH)S(34.2)


LH)S(43.15)

Isoto, End 20-60 %( LS,

LH)S(31.08)

1. Smokers exerted more isotonic

endurance limit than non-smokers

by 1.85%.

2. Highest exerted in isoto, end, 20-

60 % (HS, LH).

3. Reason: nature of experiment (low

to medium effort) and 56%

smokers and younger ages. Non

smokers Isoto, End 20-60 %( LS,

RH)NS(38.84)


RH)NS(33.12)


LH)NS(41.57)

Isoto, End 20-60 %( LS,

LH)NS(30.14)

Table 5-32 Dominancy Effect on Isotonic Endurance Limit


Dominant Isoto, End 20-60 %( LS, RH)

(38.39)


(34.12)


(41.53)


(30.66)

1. There is almost no effect for

dominancy on isotonic

endurance limit.

2. Highest isotonic endurance

limit is exerted in Isoto, end,

20-60 %( HS, LH).

Non

Dominant Isoto, End 20-60 %( LS, RH)

(37.40)


(28.9)


(53.7)


(30.70)

176

5.2 NEURAL NETWORK ANALYSIS CONCLUSION

Mean square errors (MSE) and R values for the neural network model are shown in Table

5-33 for the MVC, isometric and isotonic endurance limits. Results showed that the

neural network model provided good performance.

Table 5-33 Neural Network Summary (MVC, Isometric and Isotonic Endurance

Limits)

MVC Isometric Endurance

Limit


MSE R MSE R MSE R

7.09 e -8 9.9 e-1 3.35 e-7 9.9 e-1 1.2 e-3 9.9 e-1

1.56 e-7 9.9 e-1 3.4 e-7 9.9 e-1 6.5 e-4 9.9 e-1

7.51 e-8 9.9 e-1

2.54 e-7 9.9 e-1

2.4 e-3 9.9 e-1

Neural network performance plots are shown in Table 5-1, for the three datasets (training,

validation and testing). Validation performance was shown in Table 4-34 where best

validation performance was at 1.5 e-7 at epoch 554 for the MVC test and 3.41 e-7 at

epoch 1000 for the isometric endurance limit test and .0000655 at epoch 16 for the

isotonic endurance limit test. In this research, all results are reasonable since the final

MSEs are very small. The testing and validations errors are similar and no significant

over fitting has occurred.

In the experiments, the most errors are near zero, as viewed for the three tests (MVC,

isometric and isotonic endurance limits). The error bars is very little for all three tests:

MVC, isometric and isotonic tests. Results show that the neural network has learned and

fitted the experiment data well. The neural network model outputs accurately resemble

the experiment targets for the three datasets (training, testing, and validation).

177

5.3 ANFIS NEURAL NETWORK ANALYSIS CONCLUSION

By examining the output checking error sequences over the whole training period, it is

clear that the experiment checking dataset is very good for model validation and achieves

minimum checking error. Also, step-size errors show very small numbers which serves to

adjust references for the initial step-size and increasing and decreasing rates. Table 5-34

shows ANFIS Output Errors for the Tests (MVC, Isometric and Isotonic Endurance

Limits) and table 5-35 shows ANFIS Output Errors for Each Experimental Condition

Table 5-34 ANFIS Output Errors for the Tests (MVC, Isometric and Isotonic

Endurance Limits)

Test Results Error

MVC 3.73432 e-3 step size (0.005905)

Isometric Endurance Limits 4.2323e-05 Step size (0.008100)

Isotonic Endurance Limits 3.6203e-05 (0.006561)

Table 5-35 ANFIS Output Errors for Each Experimental Condition

Test Results Error

MVC(Kg, Sit, D) 3.84522e-05

MVC(Kg, Sit, ND) 2.46537e-05

MVC(Kg, Stand, D) 3.6203e-05

MVC(Kg, Stand, ND) 1.56111e-05

Isometric End, Limit (20%) 0.000128428

Isometric End, Limit (40%) 5.33146e-05



Isoto, End 20-60% low, SP, RH 3.00345e-05

Isoto, End, 20-60% High, SP, RH 1.73763e-05

Isoto, End, 20-60% High, SP, LH 4.6505e-05

Isoto, End, 20-60% low, SP, LH 4.61178e-05

178

5.4 Future Work

This research considers all parameters that affect the MVC, isometric and isotonic

fatigue. It has an increased importance in all aspects of job design, ergonomics and health

care research. This research recommends conducting more future studies where more

races could be included in the experiments since the literature showed great mean

differences in MVC regarding different races. For example, repeating the study using

subjects from different races could further investigate the effects of race. Additionally,

aviation female subjects could be included. Increasing the sample size might allow us to

draw a more definitive conclusion. One could also study the relationship between

subjects’ MVC and survival rates from (1) cancer, or (2) chronic kidney disease.

Similarly, the relationship between subjects’ MVC and dementia progression or walking

speed could also be studied. Future studies can include the effects of nutritional status

and bone mineral content on MVC and endurance. Also, new experiments should

consider using the new digital and computerized grip strength measurement apparatus

(e.g., grip strength reader). In addition, one could design new apparatus that measure

actual MVC in a different way than dynamometer where all independent factors can

provide more realistic measurements. Future studies could be conducted to evaluate the

palm reflexology hand therapy and include a pinch grip where researchers can better

correlate diseases with max MVC.

New and important trades should be included. For health care applications, surgery

doctors and nurses in hospitals could be studied for the effects on their performance

accuracy during operations. For engineering applications, one could recruit special

welding technicians (argon welding) as subjects to study the direct effects on their

179

performance accuracy during operations. Finally, a large number of subjects can be

studied for “strength and quality of life among critical patients, and population aging”

(Sirajudeen et al. 2012).

180

Appendices

181

APPENDIX A ANTHROPOMETRIC DATA

Gen Age Trade Smoking Weight

(Kg )

Height

(Cm)

Hand Grip

Circumference

(CM)

Forearm

Circumference

(CM)

Hand

Dom

M,F APG

Eng

E&I

COMN

AV

S, NS Weight Height HGC FAC D, ND

1 M 60 Avionic S 90 180 24 31 d

2 M 33 ENG. S 81 181 24 30 D

3 M 49 GS NS 79 193 22.5 29 D

4 M 47 ENG. NS 89 175 23.5 29 ND

5 M 56 Airframe s 97 170 21.5 30 d

6 M 44 Airframe S 75 165 22 27 d

7 M 65 Airframe s 80 168 23.5 30 d

8 M 39 Avionic NS 82 172 21.5 27 D

9 M 52 ENG. S 83 183 24.5 31.5 D

10 M 53 Airframe NS 80 176 22.5 28.5 d

11 M 43 Avionic NS 78 168 22 28 d

12 M 41 Airframe S 65 165 21 29 D

13 M 48 ENG. S 87 183 24.5 33.5 D

14 M 39 comnav NS 68 175 21.5 25.5 d

15 M 45 Airframe NS 73 173 21 26 d

16 M 41 E&I NS 95 183 22.5 34 D

17 M 44 ENG. s 110 182 25.5 35 D

18 M 32 Ground support

S 68 168 20.5 27 D

19 M 36 ENG. s 59 167 21.5 25 d

20 M 44 Airframe S 92 190 22 29 D

21 M 47 GS NS 83 170 22 28 D

22 M 39 ENG. S 77 173 24 31 D

23 M 50 Airframe NS 78 175 22 26.5 d

24 M 50 Airframe s 69 165 20 28 d

25 M 38 APG NS 70 182 21 26 d

26 M 48 ENG. NS 85 175 24 29.5 D

27 M 43 GS NS 75 186 22.5 32.5 D

28 M 36 NDI S 88 179 22 28 D

29 M 29 Avionic NS 78 173 19.5 28 D

30 M 43 ENG. NS 68 170 22.5 26.5 D

31 M 47 GS NS 90 185 23.5 31.5 D

32 M 47 APG S 81 168 22 30.5 D

33 M 37 ENG. NS 70 177 23.5 29.5 ND

34 M 49 APG S 95 185 24.5 29 D

35 M 46 ENG. S 100 178 24 33 D

36 M 52 Airframe s 68 173 22 29 d

37 M 37 ENG. S 60 168 20 25.5 D

182

38 M 46 ENG. S 105 185 24 35 D

39 M 46 GS S 76 171 22 30.5 D

40 M 36 E&I NS 78 180 22 25.5 d

41 M 43 Simulator

NS 73 167 23 30 d

42 M 50 ENG. NS 84 187 22.5 29 ND

43 M 38 Airframe s 83 178 21 27.5 d

44 M 59 Airframe NS 100 170 21.5 29.5 d

45 M 40 ENG. s 90 185 22 29 D

46 M 40 APG NS 75 170 24 31 D

47 M 42 Airframe s 83 187 22.5 32 D

48 M 44 Airframe S 105 180 24 31 d

49 M 50 Airframe NS 70 170 21 27 d

50 M 48 APG NS 92 175 22 31 D

51 M 30 APG S 61 170 20.5 26.5 D

52 M 41 Avionic S 58 160 21 25 D

53 M 23 Airframe S 70 178 23 27 D

54 M 35 Avionic NS 90 187 23 30 D

55 M 47 ENG. S 87 178 22 31 D

56 M 43 APG S 112 173 24.5 33 D

57 M 50 Airframe NS 88 178 21.5 28 d

58 M 45 ENG. S 107 181 25 35 D

59 M 46 GS S 83 170 23 29 D

60 M 34 ENG. NS 83 179 22 29 D

61 M 35 E&I NS 90 170 23 28 d

62 M 37 ENG. s 82 179 23 27 ND

63 M 45 ENG. S 63 173 22.5 26.5 ND

64 M 36 Airframe NS 86 171 21 28.5 d

65 M 35 ENG. S 75 174 23.5 29.5 D

66 M 34 ENG. NS 86 178 24 31.5 D

67 M 44 ENG. S 77 185 25.5 30 D

68 M 42 Airframe NS 82 176 22.5 29 D

69 M 34 Avionic S 72 165 21 27 D

70 M 46 ENG. NS 92 180 24.5 33.5 D

71 M 37 ENG. S 74 176 22.5 27 d

72 M 39 comnav NS 70 177 23.5 27.5 d

73 M 26 Airframe S 55 165 21 24 D

74 M 63 Avionic NS 96 172 22.5 29 D

75 M 30 Airframe S 80 165 22 29 D

76 M 43 NDI S 75 165 20 30 d

77 M 31 GS S 94 178 23 32.5 D

78 M 23 Airframe NS 72 173 21 29 D

79 M 50 Airframe NS 80 178 22 28.5 d

80 M 37 APG s 82 185 23 28 d

81 M 36 ARMT s 67 170 20 25.5 d

82 M 39 E&I NS 99 175 22 30 D

83 M 36 NDI s 96 184 23 29 ND

84 M 43 E&I NS 89 173 23 27 d

85 M 53 Airframe S 77 171 22 30 D

86 M 41 GS NS 74 155 22 28 D

87 M 35 comnav NS 93 175 24 30 d

183

88 M 48 ENG. NS 65 165 22 27.5 D

89 M 35 Airframe S 82 180 21 29 D

90 M 44 GS S 95 170 24 31.5 D

91 M 32 E&I S 90 178 21 30.5 D

92 M 43 Airframe s 85 170 22 30 D

93 M 39 comnav S 102 182 23.5 30 D

94 M 49 ENG. S 84 178 23.5 29 D

95 M 23 Airframe NS 73 180 22.5 30 d

96 M 42 ENG. S 82 182 25.5 30 D

97 M 36 NDI S 95 171 22 33 d

98 M 38 comnav S 103 185 24 32 D

99 M 38 ENG. S 70 172 23 28 D

100 M 49 ENG. NS 86 171 21.5 29.5 ND

101 M 29 Airframe NS 84 180 21.5 29 d

102 M 37 Airframe NS 78 169 21 26 D

103 M 47 ENG. S 99 184 24.5 35 D

104 M 40 ENG. NS 85 185 22.5 30 D

105 M 37 ENG. S 65 175 23 29 D

106 M 50 ENG. NS 88 180 25 31.5 D

107 M 30 GS NS 61 171 22 23 d

108 M 49 Airframe S 74 177 22 27 D

109 M 39 GS s 95 188 23 33 D

110 M 39 GS NS 68 178 21.5 25 D

111 M 26 Airframe S 94 180 21.5 29.5 d

112 M 24 Airframe S 74 173 22 28 D

113 M 38 Comnav NS 114 193 24 31 d

114 M 46 ENG. NS 95 173 22.5 31 ND

115 M 38 Airframe S 105 183 23 31.5 d

116 M 47 ENG. NS 100 170 22.5 31.5 D

117 M 48 ENG. S 98 181 23 33 ND

118 M 42 Airframe S 73 176 20.5 27 d

119 M 45 Airframe NS 67 170 22 29 d

120 M 47 Airframe s 81 181 24 29.5 d

121 M 48 ENG. S 88 188 24.5 27 D

122 M 29 ENG. S 56 173 20.5 26.5 D

123 M 45 GS S 81 183 22 31 D

124 M 45 GS NS 94 175 24 31.5 D

125 M 42 ENG. NS 107 181 24.5 33 D

126 M 47 ENG. S 64 165 22 27.5 D

127 M 49 Airframe NS 100 179 24 33 d

128 M 46 ENG. NS 90 172 24 29.5 ND

129 M 48 Airframe S 60 160 20 28 d

130 M 34 Airframe NS 85 176 22 32 d

131 M 27 Airframe S 95 184 24.5 34 D

132 M 50 Airframe s 76 174 20 27.5 d

184

APPENDIX B: MVC DATA

# Max (MVC)Kg Max (MVC)Kg Max (MVC)Kg Max (MVC)Kg

MAX(MVC)

SITTING,

Right Hand

MAX(MVC)

SITTING,

Left Hand

MAX(MVC)

STANDING,

Right Hand

MAX(MVC)

STANDING,

Left Hand

1 51 50 53 46

2 61.1 60.5 57.4 59.1

3 45.8 41.3 44.5 17.1

4 43.2 46.1 43.6 43.2

5 24.6 28 24.8 21.6

6 37.7 35.3 36.5 34

7 37.5 35.7 39.6 36

8 45.2 40 47 40.9

9 47.9 43.6 51.7 45.6

10 46.3 43.4 47 46.5

11 45 43.6 48.2 46.3

12 37.6 34.4 43.7 37.4

13 46.6 50.7 54.6 49.9

14 40.1 38.2 41.2 37.8

15 46 41.8 48 48.9

16 55.2 47.3 58.8 49.6

17 56.8 43 60.8 47.4

18 48 47.7 53 42.8

19 38.2 34.7 36.8 39.1

20 59.3 54.7 61.2 60.3

21 47 50 41 45

22 49.9 46.3 57.7 48.7

23 38.9 34.5 38 32.5

24 40 50 43.3 40.4

25 52.3 44.2 54.7 46.5

26 41 46.4 38.9 44.2

27 55.3 54.5 54.3 50.3

28 43.7 45.8 40.8 40.9

29 43.7 45 47.2 42.7

30 40.6 34 38.9 31

31 51.3 52.3 59.1 54.3

32 41.1 45.8 44.7 42.3

33 55.6 57.7 52.1 51.3

34 46.5 46.4 42.7 45.8

35 55.7 54.4 60.9 56.8

36 45 40 50.1 44.1

37 43 38.2 37.6 34.2

38 54.9 41.4 59 45.3

39 39.2 44.2 42.9 40.2

40 40 38.5 38.5 34.5

185

41 51.7 46.5 55.1 22.8

42 47.7 42.9 46.3 19.2

43 50.3 43 49.3 41.9

44 33 29 35 31

45 51.6 50 53.2 51.1

46 51.8 47.9 59.5 50.8

47 49.2 45.1 49.4 45.1

48 48.1 42.5 54 47.2

49 40.2 37.3 40.4 33.2

50 45 46 48.3 49.5

51 52.4 57.9 51.4 52.1

52 41 35.4 44.7 41.5

53 37.5 48.8 42.5 47

54 50 45.8 54.1 47.7

55 43.1 44.4 46.5 47.4

56 44.8 50.7 48.3 48.7

57 47 34 44.3 37.7

58 58.9 54.5 66.3 56.2

59 33.1 39.6 49.3 47.2

60 45 51.6 48.4 55.1

61 44.4 44.7 50.3 45.5

62 42 38.2 43.4 37.7

63 38.7 32.4 37.1 28.9

64 35 39 41 39

65 57.5 59.3 53.9 53.4

66 63 62.1 59.2 61.2

67 42.7 38.2 42.4 38.3

68 53.7 56 57.8 57.4

69 40.1 38.1 41.7 33.4

70 48.5 52.3 56.4 52

71 46.8 42 46 37

72 52.4 58 51 54.2

73 36.5 36.7 42.6 39.5

74 29.8 27 29.7 27

75 44.6 42.3 47.4 43.7

76 50.9 54.5 51 38.4

77 47.5 45 54.9 45.3

78 53.9 51.9 54.8 51

79 43 43 43 45

80 48.7 47.6 57.2 49.8

81 42.7 40.6 43.3 38.7

82 48.4 42 50.1 44.8

83 57 45 57 36.4

84 49.9 43.7 50.9 46.4

85 37.5 34.5 35.7 35.1

86 39.2 35.5 37.2 41.3

87 53.4 51.6 49.1 50

88 42.9 39.7 46.8 41.5

89 55.5 51.6 49.9 53.4

90 54.7 55.1 46.6 55.6

91 50.2 51.3 50 55.2

186

92 45 40.2 46.2 45.9

93 51.2 44.3 52.7 46.6

94 41.3 44.5 41.8 41.1

95 57.7 61.4 59.3 57

96 44.6 39.8 44.2 40.4

97 55 56 49 53

98 51.4 49.5 48.5 47.3

99 46.3 45 51.7 49.4

100 44 43.6 45.2 45

101 51.6 50.2 49.6 48

102 44.5 46.8 45.5 47.1

103 57 52.9 64.5 54.1

104 54.7 50.4 60.3 60.1

105 44.4 43.4 49.9 47.3

106 49.8 45.2 53.5 47.7

107 38 40.8 39.4 39.7

108 38 42.3 39 44.3

109 60.1 58.9 61 60.6

110 39.9 35.9 40.8 31

111 45 48 49 47.6

112 43.3 58.5 55.2 49.6

113 67.6 68 61.5 64.9

114 35.2 35.9 36.8 42.1

115 68 64.6 60 49

116 37.1 37.5 38.6 44.2

117 53.8 52.8 59.1 54.7

118 57 50.8 60.5 48

119 58 55.3 61 51.4

120 55.5 43.9 55 46

121 44.6 44.8 40.9 43.7

122 50.5 56.3 49.6 50

123 54.1 53.2 50.2 50.1

124 53.9 48 63 44

125 42.9 49.1 46.5 46.6

126 41 38.1 45 39.4

127 44.5 46.5 36 45

128 42.9 48 40.7 46.3

129 30 32 37 38

130 52 52 56 51.8

131 81.6 77.8 78.3 68.2

132 54 50.4 49 42

187

APPENDIX C: ISOTROMIC ENDURANCE LIMIT MVC DATA

#

20%

MVC

MAX (Isometric

Endurance

Limit

(Total) (Sec)

40%

MVC

MAX

(Isometric

Endurance

Limit

(Total) (Sec)

60%

MVC

MAX (Isometric

Endurance Limit

(Total) (Sec)

80%

MVC

MAX(Isometri

c Endurance

Limit

(Total) (Sec)

TIME 20%

MVC

TIME 40%

MVC

TIME 60%

MVC

TIME 80%

MVC

1 10.2 115 20.4 56 30.6 23 40.8 16

2 12.22 343 24.44 69 36.66 60 48.88 18

3 9.16 75 18.32 31 27.48 16 36.64 9

4 8.64 300 17.28 53 25.92 25 34.56 14

5 4.92 187 9.84 96 14.76 43 19.68 13

6 7.54 90 15.08 32 22.62 18 30.16 13

7 7.5 187 15 122 22.5 100 30 93

8 9.04 125 18.08 22 27.12 18 36.16 7

9 9.58 118 19.16 54 28.74 30 38.32 23

10 9.26 190 18.52 66 27.78 21 37.04 12

11 9 88 18 50 27 15 36 9

12 7.52 191 15.04 67 22.56 22 30.08 17

13 9.32 293 18.64 83 27.96 28 37.28 22

14 8.02 90 16.04 29 24.06 23 32.08 8

15 9.2 227 18.4 112 27.6 57 36.8 41

16 11.04 211 22.08 102 33.12 67 44.16 45

17 11.36 240 22.72 58 34.08 27 45.44 20

18 9.6 199 19.2 133 28.8 40 38.4 33

19 7.64 183 15.28 37 22.92 13 30.56 5

20 11.86 189 23.72 81 35.58 68 47.44 26

21 9.4 113 18.8 73 28.2 32 37.6 20

22 9.98 233 19.96 53 29.94 30 39.92 18

23 7.78 145 15.56 98 23.34 33 31.12 19

24 8 230 16 75 24 35 32 24

25 10.46 220 20.92 39 31.38 19 41.84 11

26 8.2 136 16.4 51 24.6 32 32.8 15

27 11.06 180 22.12 92 33.18 43 44.24 29

28 8.74 98 17.48 59 26.22 16 34.96 11

29 8.74 200 17.48 116 26.22 65 34.96 22

30 8.12 180 16.24 54 24.36 50 32.48 15

31 10.26 93 20.52 72 30.78 39 41.04 25

32 8.22 180 16.44 55 24.66 31 32.88 18

33 11.12 216 22.24 45 33.36 18 44.48 10

34 9.3 100 18.6 36 27.9 17 37.2 12

35 11.14 314 22.28 35 33.42 12 44.56 15

36 9 200 18 90 27 73 36 29

37 8.6 139 17.2 112 25.8 34 34.4 12

38 10.98 253 21.96 67 32.94 34 43.92 23

39 7.84 193 15.68 64 23.52 38 31.36 21

188

40 8 180 16 42 24 12 32 9

41 10.34 75 20.68 45 31.02 15 41.36 14

42 9.54 62 19.08 22 28.62 9 38.16 6

43 10.06 128 20.12 23 30.18 18 40.24 9

44 6.6 99 13.2 21 19.8 12 26.4 11

45 10.32 135 20.64 79 30.96 49 41.28 40

46 10.36 220 20.72 44 31.08 23 41.44 15

47 9.84 179 19.68 96 29.52 52 39.36 29

48 9.62 123 19.24 97 28.86 30 38.48 21

49 8.04 180 16.08 89 24.12 40 32.16 23

50 9 185 18 27 27 20 36 13

51 10.48 80 20.96 47 31.44 19 41.92 11

52 8.2 121 16.4 99 24.6 49 32.8 31

53 7.5 160 15 101 22.5 39 30 37

54 10 153 20 89 30 53 40 35

55 8.62 198 17.24 36 25.86 27 34.48 16

56 8.96 186 17.92 81 26.88 30 35.84 12

57 9.4 150 18.8 54 28.2 22 37.6 14

58 11.78 190 23.56 64 35.34 31 47.12 20

59 6.62 97 13.24 39 19.86 27 26.48 16

60 9 130 18 83 27 54 36 26

61 8.88 190 17.76 27 26.64 19 35.52 10

62 8.4 81 16.8 35 25.2 12 33.6 8

63 7.74 193 15.48 63 23.22 57 30.96 18

64 7 126 14 98 21 80 28 47

65 11.5 203 23 36 34.5 11 46 7

66 12.6 330 25.2 60 37.8 53 50.4 15

67 8.54 193 17.08 42 25.62 29 34.16 14

68 10.74 172 21.48 109 32.22 41 42.96 18

69 8.02 176 16.04 70 24.06 41 32.08 35

70 9.7 280 19.4 74 29.1 21 38.8 19

71 9.36 300 18.72 203 28.08 99 37.44 45

72 10.48 112 20.96 29 31.44 20 41.92 7

73 7.3 129 14.6 99 21.9 42 29.2 15

74 5.96 219 11.92 87 17.88 39 23.84 35

75 8.92 188 17.84 111 26.76 35 35.68 15

76 10.18 200 20.36 90 30.54 60 40.72 22

77 9.5 76 19 72 28.5 32 38 27

78 10.78 113 21.56 91 32.34 47 43.12 28

79 8.6 120 17.2 79 25.8 39 34.4 17

80 9.74 112 19.48 52 29.22 17 38.96 10

81 8.54 86 17.08 44 25.62 14 34.16 10

82 9.68 165 19.36 28 29.04 20 38.72 8

83 11.4 223 22.8 190 34.2 97 45.6 33

84 9.98 116 19.96 36 29.94 12 39.92 6

85 7.5 112 15 89 22.5 64 30 39

86 7.84 189 15.68 102 23.52 48 31.36 33

87 10.68 156 21.36 25 32.04 20 42.72 6

88 8.58 240 17.16 140 25.74 70 34.32 11

89 11.1 99 22.2 45 33.3 44 44.4 43.8

90 10.94 102 21.88 44 32.82 31 43.76 16

189

91 10.04 135 20.08 105 30.12 44 40.16 25

92 9 121 18 91 27 58 36 21

93 10.24 187 20.48 31 30.72 21 40.96 8

94 8.26 313 16.52 62 24.78 32 33.04 17

95 11.54 177 23.08 143 34.62 54 46.16 44

96 8.92 180 17.84 33 26.76 22 35.68 11

97 11 176 22 112 33 78 44 50

98 10.28 170 20.56 42 30.84 22 41.12 11

99 9.26 180 18.52 60 27.78 19 37.04 12

100 8.8 90 17.6 56 26.4 23 35.2 12

101 10.32 140 20.64 103 30.96 37 41.28 28

102 8.9 86 17.8 76 26.7 70 35.6 63

103 11.4 203 22.8 73 34.2 38 45.6 23

104 10.94 187 21.88 90 32.82 66 43.76 32

105 8.88 193 17.76 69 26.64 26 35.52 15

106 9.96 105 19.92 45 29.88 23 39.84 20

107 7.6 93 15.2 87 22.8 69 30.4 50

108 7.6 99 15.2 80 22.8 39 30.4 25

109 12.02 203 24.04 87 36.06 50 48.08 33

110 7.98 100 15.96 54 23.94 41 31.92 35

111 9 211 18 115 27 26 36 12

112 8.66 210 17.32 176 25.98 74 34.64 18

113 13.52 139 27.04 28 40.56 18 54.08 10

114 7.04 189 14.08 73 21.12 40 28.16 28

115 13.6 219 27.2 101 40.8 30 54.4 22

116 7.42 176 14.84 64 22.26 33 29.68 25

117 10.76 327 21.52 44 32.28 19 43.04 18

118 11.4 183 22.8 112 34.2 58 45.6 40

119 11.6 139 23.2 105 34.8 45 46.4 25

120 11.1 250 22.2 145 33.3 90 44.4 43

121 8.92 113 17.84 45 26.76 24 35.68 15

122 10.1 93 20.2 56 30.3 26 40.4 14

123 10.82 172 21.64 87 32.46 41 43.28 25

124 10.78 60 21.56 40 32.34 25 43.12 18

125 8.58 199 17.16 90 25.74 37 34.32 15

126 8.2 253 16.4 149 24.6 77 32.8 14

127 8.9 112 17.8 55 26.7 21 35.6 12

128 8.58 123 17.16 42 25.74 25 34.32 12

129 6 95 12 80 18 36 24 32

130 10.4 190 20.8 119 31.2 97 41.6 72

131 16.32 157 32.64 150 48.96 116 65.28 17

132 10.8 256 21.6 90 32.4 65 43.2 30

190

APPENDIX D ISOTONIC MVC DATA

MAX(Isotonic

Muscle Fatigue Test

(Total) (Sec)

MAX(Isotonic Muscle

Fatigue Test (Total) (Sec)

MAX(Isotonic Muscle Fatigue

Test (Total) (Sec)

MAX(Isotonic

Muscle Fatigue

Test (Total)

(Sec)

20%-60% low Right 20%-60% high Right 20%-60% low left 20%-60% high

left

1 19 15 12 9

2 45 71 109 65

3 13 18 13 11

4 45 14 98 57

5 33 30 31 29

6 32 31 39 37

7 67 69 59 41

8 40 24 40 21

9 37 52 48 47

10 56 51 40 38

11 29 23 23 20

12 25 27 21 13

13 42 40 67 34

14 60 30 53 28

15 42 43 21 20

16 44 41 45 47

17 49 70 60 30

18 40 39 39 42

19 26 28 45 24

20 27 29 28 29

21 20 20 18 15

22 60 47 63 36

23 39 33 27 25

24 85 80 18.8 26

25 52 30 58 28

26 42 32 70 36

27 29 24 22 17

28 22 17 31 20

29 29 29 30 23

30 41 28 34 27

31 35 32 39 36

32 29 30 62 45

33 36 37 90 34

191

34 29 21 45 34

35 41 35 88 42

36 92 80 60 55

37 13 11 9 9

38 56 77 64 33

39 36 37 66 48

40 28 26 80 85

41 25 19 27 19

42 6 11 9 8

43 41 26 46 22

44 39 31 30 21

45 37 31 30 24

46 53 40 59 33

47 28 31 29 23

48 47 43 39 33

49 110 75 99 71

50 49 24 42 35

51 21 17 60 28

52 45 40 46 39

53 50 47 43 39

54 33 30 33 24

55 56 31 46 38

56 44 51 67 40

57 21 27 20 23

58 35 48 75 56

59 20 22 21 16

60 39 40 29 26

61 45 27 43 26

62 27 22 32 21

63 48 35 38 30

64 22 19 19 15

65 29 30 86 31

66 38 64 105 62

67 47 28 56 26

68 18 19 13 12

69 30 23 29 28

70 35 33 63 31

71 12 11 10 9

72 38 25 29 21

73 20 19 17 19

74 40 42 38 30

75 36 31 29 31

76 43 40 40 34

77 31 30 25 24

78 34 32 28 29

79 23 21 31 21

80 58 18 75 34

81 38 24 36 22

82 49 25 42 20

83 32 23 29 21

84 56 32 50 35

192

85 51 53 49 56

86 26 29 30 31

87 42 26 40 25

88 18 56 40 43

89 50 41 32 34

90 40 31 25 21

91 24 24 21 17

92 32 28 31 33

93 54 27 46 23

94 52 21 102 60

95 40 29 26 29

96 40 21 52 23

97 49 40 37 33

98 41 24 43 24

99 48 33 45 49

100 23 19 15 11

101 30 24 21 17

102 59 58 50 44

103 42 55 79 59

104 31 29 27 21

105 55 40 49 52

106 30 45 44 44

107 50 45 41 39

108 37 38 31 30

109 31 32 25 27

110 42 41 40 41

111 10 9 9 7

112 44 32 31 21

113 32 24 48 15

114 74 61 68 47

115 19 18 18 14

116 67 54 64 44

117 48 42 92 45

118 21 19 17 15

119 21 17 15 10

120 47 40 41 33

121 36 28 49 37

122 28 24 64 31

123 31 26 30 21

124 25 19 13 11

125 51 58 71 43

126 25 63 44 46

127 45 41 33 29

128 35 25 66 33

129 30 30 27 25

130 59 50 49 41

131 29 25 26 21

132 11 10 9 7

193

Intentionally Left Blank

194

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