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GAIT PARAMETERS DETERMINATION BY 3D

MOTION ANALYZER SYSTEM FOR INITIAL

INDONESIAN GAIT DATABASE

Nuha Desi Anggraeni1

, Ferryanto1, Suryo Tri Atmojo

1, Sandro Mihradi

1, Tatacipta

Dirgantara2, Andi Isra Mahyuddin

1

1Mechanical Design Research Group, Mechanical Engineering Department

2Lightweight Structures Research Group, Aeronautics & Astronautics Department

Faculty of Mechanical and Aerospace Engineering, Institut Teknologi Bandung

Jalan Ganesha 10, Bandung 40132, Indonesia,

Tel., +62 22 2504243, Email: aim@ftmd.itb.ac.id

Abstract

A 3D motion analyzer system to determine gait parameters as well as kinematics and kinetics of human

walking was developed previously. The system utilizes two 90 fps cameras, a personal computer and in-

house software developed to determine gait parameters of human walking motion as well as the

kinematics and kinetics of gait. The objectives of this research are two fold. First objective is to evaluate

the efficacy of the developed system and second objective is to obtain initial Indonesian normal gait

database. Spatio-temporal, kinematic, and kinetic parameters of 60 subjects (30 male, 30 female) obtained

by the 3D motion analyzer system are presented as preliminary results in an effort to establish Indonesia

gait database. Prior to measurement, each subject was evaluated to ensure normalcy of posture, and then

was instructed to walk normally in a specially-arranged walking area. The gait data obtained are

comparable to available normal gait data. This indicates that the initial development of the 3D normal gait

database is quiet successful. The data presented in this research could serve as the basis to establish

reference for 3D Indonesian normal gait database. The system developed could be further utilized in the

enrichment of the database as well as for clinical purpose by measuring and analyzing abnormal gait. The

resulting kinematic and kinetic parameters are useful in determining therapy protocol as well as keeping

track of the patient‟s progress. Hence, the system has the potential to be further developed into a medical

diagnostic tool.

Keywords: 3D Gait Parameters, 3D Motion Analyzer System, Gait Analysis, Gait Database

Introduction

Human movement analysis has long been studied and found applications in various fields, such

as medical rehabilitation, medical diagnostic, and sport science [1-5]. In medical rehabilitation,

information about position and orientation of various joints of a patient body is needed to

determine abnormalities. It is also reported that a knowledge of the expected changes in joint

angle allow therapists to better understand a patient gait pathology [6-7].

In general, human movement could be measured by direct measurement techniques and also

imaging (optical) measurement techniques. The main problem in direct measurement techniques

is the subject has to carry many cables or other components that could affect walking motion [5].

Most of the problems encountered by direct measurement techniques could be overcome by

imaging (optical) measurement techniques.

Unfortunately, human movement analyzer is not available in most Indonesian hospitals due to

its relatively high cost. Moreover, there is also no Indonesian reference data that could be used to

diagnose whether a patient has a normal or pathological gait.

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Hence, an affordable motion analyzer system to obtain human gait parameters has been

developed. First, a system for 2D kinematics and kinetic analyses of human gait was developed

[8-9]. The system is then used to determine 2D gait parameters of Indonesian people as part of an

effort to develop the first Indonesian gait database [10-11].

Information obtained from 2D measurement is limited to the sagittal plane, while very useful

and in most cases are adequate, do not provide motion information in other planes. Although the

sagittal plane is probably the most important one, where much of the movement parameters could

be observed, there are certain gait pathologies where another plane (e.g. the frontal plane) would

yield useful information. Therefore, a 3D motion analyzer system is developed using similar

optical methodology [12-15]. In this work, 3D gait parameters based on optical measurement is

conducted to establish initial Indonesian gait reference data for medical diagnostic tool.

3D Motion Analyzer System

This work uses a previously developed 3D motion analyzer system consists of image processing

system, and kinematic and kinetic analyzer for human gait developed as reported in Mihradi et al.

[12-13]. As shown in Figure 1, the system consists of two 90 fps (frame per second) video

cameras, a personal computer, and computing software that was developed for tracking markers

attached to a human subject during motion and determining the real coordinates of markers along

their trajectories. Another software based on a multibody model of human, is also developed to

calculate gait parameters of human gait as well as the kinematics and kinetics of gait, based on

the markers real coordinates. Prior to recording the subject walking motion, a calibration

procedure is conducted to acquire camera parameters required for 3D reconstruction. The

calibration for this 3D motion analyzer system uses modified Zhang‟s technique proposed by

Ferryanto et al. [14].

Figure 16.Experimental setup.

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The markers positioning and human body model employed in this study are described in more

detail by Mihradi et al. [15]. Seven markers are attached on seven positions of right-side subject‟s

leg, i.e. pelvis, hip, mid-thigh, knee, tibia, malleolus, and lateral metatarsal. Marker locations on

the subject body are depicted in Figure 2. The image of the markers are processed to acquire their

real coordinate in 3D Cartesian, which are then employed as input for kinematic and kinetic

analyses. As in Mihradi et al. [15], the analyzer utilizes an eight segments human body model

(Figure 3a) consisting of right foot, left foot, right calf, left calf, right thigh, left thigh, pelvic and

upper body, which is represented by one segment called HAT (head, arm, and trunk). All

segments are linked as a multibody system by seven joints: right ankle, left ankle, right knee, left

knee, right hip, left hip, and lumbar joint, depicted as blue dots in Figure 3b.

Figure 17.Markers location on the subject.

Figure 18.a) Eight segments human body model. b) Skeleton model of human body consists of

eight rigid bodies and seven joints (blue dot).

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Participating Subject and Data Collection

Spatio-temporal, kinematic, and kinetic parameters of 60 participating subjects (30 male, 30

female) obtained by the 3D motion analyzer system are presented as preliminary results in an

effort to establish Indonesia gait database. The subjects‟ weight, height, and anthropometric data

are measured and the results are summarized in Table 1.

Table 5. Age and Anthropometric Data of Participating Subjects

Variable Male Female

Mean (SD) Mean (SD)

Age (year) 21.87 (2.62) 22.47 (4.22)

Weight (Kg) 64.80 (9.14) 52.73 (6.46)

Height (m) 1.73 (0.09) 1.59 (0.06)

ASIS breadth (m) 0.32 (0.042) 0.31 (0.041)

Upper thigh diameter (m) 0.13 (0.012) 0.12 (0.013)

Midthigh circumference (m) 0.45 (0.087) 0.47(0.041)

Thigh length (m) 0.42 (0.038) 0.41 (0.049)

Knee diameter (m) 0.10 (0.008) 0.09 (0.008)

Calf circumference (m) 0.37 (0.031) 0.34 (0.024)

Calf length (m) 0.42 (0.032) 0.40 (0.025)

Malleolus width (m) 0.07 (0.009) 0.07 (0.009)

Foot width (m) 0.12 (0.007) 0.21 (0.028)

Malleolus height (m) 0.08 (0.004) 0.07 (0.006)

Foot length (m) 0.26 (0.047) 0.24 (0.012)

BMI 21.63 (2.77) 20.75 (1.93)

To check for normalcy, similar procedures as the ones conducted in Mahyuddin et al. [11]

were followed. Before the measurements, body posture and body mass index (BMI) of each

subject is evaluated to ensure normalcy. In order to assess posture normalcy, the shoulder and the

back of each subject are observed (see Figure 4). If it is unsymmetrical, it will be considered

abnormal that could be due to scoliosis or lordosis. The length of each leg is also measured to

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check whether the two legs are having the same length (see Figure 4). A difference of more than

2 cm will be considered abnormal. Height and weight of each subject are measured to calculate

the BMI, which is considered normal if the value is between 18.5 and 24.9. Only subjects with

normal posture and BMI participated in the gait measurement.

Figure 19. Posture normalcy assessment and anthropometric measurement.

Results and Analysis

Following are several results obtained from the measurements of the subjects. The spatio-

temporal, kinematics, and kinetic parameters of the subjects are presented and then compared to

those obtained from other researches and literatures.

Spatio-Temporal Parameters

The spatio-temporal parameters, i.e. cadence, stride length, walking speed, and cycle time of the

subjects are summarized in Table 2.

Table 6. Spatio-Temporal Gait Parameters of Subjects

Variable Male Female

Mean (SD) Mean (SD)

Cadence (step/min) 84.13 (7.75) 84.97 (5.26)

Cycle period (s) 1.44 (0.13) 1.42 (0.09)

Stride length (m) 1.14 (0.12) 1.09 (0.11)

Walking speed (m/s) 0.8 (0.13) 0.75 (0.16)

From Table 2, it may be seen that the cadence of female subjects are slightly higher than male

subjects, while the stride length on male subjects are longer than female subjects. Differences in

male and female subjects may be attributed to the differences in anthropometric. Since male

subjects are taller than female, thus the stride length on male subjects are relatively longer than

female subjects. On the other hand, female tends to have a higher cadence than male. But, male

average walking speed is higher than female.

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In order to check for the validity of the 3D analyzer, the results obtained in the present work

are compared to the Indonesian normal 2D gait parameters for subjects in the same age-group

presented in Mahyuddin et al. [10]. Since no 3D Indonesian gait data is available, the results are

also compared to the Korean 3D gait parameters presented in Ryu [16]. Figures 5 and 6, present

the comparison of the spatio-temporal parameters, stride length and walking speed, respectively.

Figure 20. Comparison of stride length.

Figure 21.Comparison of walking speed.

Spatio-temporal parameters are then analyzed statistically using Fisher Test and T-test in

order to know whether the differences are significant or not. In this research, significance level α

= 0.05 was chosen. The detail of the analysis is given in Atmojo [17] based on test criterion

presented in Majernik [18].

From the statistical analysis of the spatio-temporal parameters obtained in this work and the

ones reported by Mahyuddin et al. [10], it may be concluded that almost all spatio-temporal

parameters differ significantly, except the stride length of female subject. The differences may be

attributed to the differences in anthropometric of the subject participating in the present work to

the presented in [10].

The difference of spatio-temporal parameters especially walking speed may be also caused by

the limitation of the length of the walking path. The ideal length of walking path is 10 m, as

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reported by Whittle [4]. The 10 m walking path should provide the first two steps for the

acceleration to steady walking speed and the last two steps for deceleration until the subject stop.

However, the length of walking speed of present work is only 3 m and could only provide

approximately 3 steps. As a result, subjects may have to decrease their speed while they are

accelerating their walking speed. Hence, the walking speed of the subject in this study may not

yet reach steady gait. Further measurement with longer walking path are ongoing.

Kinematic Parameters

In the present work, the joint kinematics of the human body model may be determined by using

the developed software. As examples of the results, joint kinematics in the form of knee and

ankle angles in the three reference planes are presented, in Figures 7 and 8, respectively. The

knee and ankle angles are compared qualitatively with those reported by Vaughan et al. [19].

From Figures 7 and 8, it may be seen that the kinematic parameters pattern qualitatively show

good agreement between present work and that of Vaughan et al. [19]. However, there are

differences between them which may be caused by insufficient number of markers in the present

work. Furthermore, instability of markers while subject is walking may also affect the

measurement, and consequently the kinematic parameters evaluation. This issues are also

investigated at present.

Figure22. Knee angle (a) present work (b) Vaughan et al. [19]

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Figure 23. Ankle angle (a) present work (b) Vaughan et al. [19]

Kinetic Parameters

All kinetic parameters, i.e. ground reaction forces, joint forces, ground reaction moments, and

joint moments may be evaluated by employing the eight segments human body model and the

inertia and anthropometric data presented in Table 2. As example, ground reaction forces in the

three axes are presented in Figure 9, along with the ones obtained by Ren et al. [20].

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Figure 24. Ground reaction force comparison (a) present work (b) Ren et al. [20]

While realizing that the subjects in Ren et al. [20] are different than the ones participating in

the present work, the comparisons are intended as qualitative. From Figure 9, it may be seen that

the parameters are not exactly the same as those from the Ren et al. [20], but the trends of these

curves are very similar. Based on this, the 3D motion analyzer system developed is deemed to

have the potential to be employed in 3D gait analysis. Even though steps to ensure the accuracy

of the measurement need to be taken.

Conclusion

In this work, measurement of human gait parameters by using 3D motion analyzer system is

presented. The 3D motion analyzer system consists of an image processing system and kinematic

and kinetic analyses software. The system uses two 90 fps cameras to obtain markers trajectory

and convert the markers positions to real world coordinates. These results are then employed by

the kinematics and kinetics analyzer to obtain gait and kinetic parameters. 30 male and 30 female

subjects participated in this work. The results could be considered as initial normal gait database

of Indonesian people. The data are comparable to available normal gait data. This indicates that

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the initial development of the database is quite successful. The obtained data should serve as

initial database, while the system developed could be further utilized in the enrichment of the

database as well as for clinical purpose by measuring and analyzing abnormal gait. The resulting

kinematics and kinetic parameters are useful in determining therapy protocol as well as keeping

track of the patient‟s progress. Hence, the system has the potential to be further developed into a

medical diagnostic tool.

Acknowledgement

The authors gratefully acknowledge the support from Program Riset Desentralisasi DIKTI 2013

for the present work.

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