$168 Journal of Biomechanics 2006, Vol. 39 (Suppl 1)

5.11. Occupational Disorders, Repetitive Strain Injury 5392 Tu, 11:45-12:00 (P18) Tracking slow-time-scale changes in human locomotion

J.B. Dingwell 1 , D.F. Napolitano 2, D. Chelidze 2. 1Nonlinear Biodynamics Lab, Dept. of Kinesiology, University of Texas, Austin, TX, USA, 2Dept. of Mechanical Engineering, University of Rhode Island, Kingston, RI, USA

Degenerative processes like osteoarthritis and repetitive strain injuries cause normal movement patterns to change slowly over time. Accurately tracking how these processes evolve could allow early intervention and thus prevent further deterioration. However, these processes usually cannot be measured directly and first-principles predictive models are highly complex and difficult to derive analytically. Our goal is to develop methods to track such "hidden" biological processes from easily obtainable biomechanical data. Our approach is derived from a method for tracking mechanical fatigue failure [1,2]. We assume our system is composed of a fast-time-scale sub-system of observable states, x(t), and a slow-time-scale sub-system of hidden states, ~,(t). We use state space data from the unperturbed system to construct a local linear model of x(t). Drift in ~,(t) leads to "errors", Ek, between actual and predicted behavior. We compute E k directly from the data and define a scalar tracking metric, ~(t), as the average error over time in our model of x(t), as introduced by q,(t). To test our method, 5 healthy subjects walked on a motorized treadmill at their self-selected pace. The treadmill incline was increased from 0 ° to +80 slowly over 25 minutes. Sagittal plane hip, knee and ankle angles were recorded continuously at 60 Hz to define x(t). Tracking metrics, ~(t), were computed from joint angle data. Cubic regressions were used to determine how well tracked the "hidden" treadmill incline. Basic patterns of joint kinematics changed little across trials. Tracking metrics (~) increased monotonically with treadmill angle for all subjects (88.9% < r 2 < 98.2%). The proposed method yields valid measures of slow-time-scale changes in biomechanics, without the need for "guessing" or highly detailed first-principles models [2].

References [1] Chatterjee A, et al. J. Sound & Vibr. 2002; 250: 877-901. [2] Chelidze D, et al. J. Vibr. & Acoust. 2002; 124: 250-264.

4359 Mo, 16:15-16:30 (P12) Analysis of mechanical stimuli on mechanoreceptors in a fingertip exposed to vibrations J.Z. Wu, K. Krajnak, D.E. Welcome, R.G. Dong. National Institute for Occupational Safety and Health, Morgantown, W~, USA

Exposure to vibration can result in a temporal increase in vibration perception threshold. The vibrations applied at the fingertip are detected by the Meissner's and Pacinian corpuscles, which are located in superficial and deep zone in skin, respectively. The purpose of the present study is to analyze the frequency-dependent dynamic deformations in tissues that the Meissner's and Pacinian corpuscles sense during vibrations, thereby elucidating the mecha- nism of vibration perception of human fingers. The analysis was performed using a multi-layered 2D finite element model, which includes a skin layer, subcutaneous tissue, bone, and nail. The skin and subcutaneous tissues were assumed to be nonlinearly elastic and viscoelastic, while the bone and nail were considered as linearly elastic. The simulation procedure was performed in two stages: static pre-compression and steady-state dynamic analysis. The frequency-dependent distributions of the vibration magnitude in the tissues are predicted in a frequency domain ranged from 16 to 2000Hz. Our simulations showed that the vibration exposure factors for the Pacinian corpuscles first increase with increasing frequency, reach peak values around 125-250 Hz, and then decrease substantially for frequencies greater than 500 Hz, while those for the Meissner's corpuscles decrease consistently with increasing vibration frequency. Our predictions indicated that the vibration exposure at a frequency range from 63 to 250 Hz will induce excessive dynamic strain in the deep zone of the finger tissues, effectively inhibiting the high-frequency mechanoreceptors (Pacinian); while the vibration exposure at low frequency (less than 31.5 Hz) tends to induce excessive dynamic strain in superficial layer in the tissues, inhibiting the low-frequency mechanoreceptors (Meissner). The present study is the first theoretical analysis of the frequency-dependent distributions of dynamic strains in fingertip during vibrations, and the model predictions are consistent with the experimental observations (Harada and Griffin, 1991, Br J Ind Med 48: 185-92).

Oral Presentations

6752 Mo, 16:30-16:45 (P12) Micromotion identification, isolation and associated loading

A. Moore 1 , I. Kudryk 2. 1School of Kinesiology and Health Science, York University, Toronto, Canada, 2School of Physical and Health Education, Queen's University, Kingston, Canada

Linking engineering methods used in production design with risk of injury is an ideal goal in a proactive risk management system (e.g. Laring et al, 2005). While predictions of posture and hand grip forces have been used, less is known about the link between specific micromotions and muscle activity. The purpose of this study was to isolate each of the micromotions included in a highly repetitive task and measure the associated loading. Eight male partici- pants performed a hand transfer task involving five micromotions: reach, grasp, turn, move, position. The task was performed using a 2.0 Kg part at a standard- ized cycle time, controlled by auditory signal. Surface electromyographic (EMG) signals were measured from seven upper limb muscles. Hand grip force, wrist and elbow posture, and a series of photoelectric and pressure switch signals were simultaneously collected and were used to identity the micromotions. Custom software was used to partition the EMG, force and posture data by micromotion. Amplitude Probability Distribution Function and Gaps analyses were performed for the complete task and for each micromotion. Micromotions ranged from 2.2-18.5% of the total time. Partitioning allowed for identification of higher loading activities. For example, static (10 th percentile) EMG for the flexor digitorum superficialis was 4.2% MVC for the overall task, ranging from 20.9% MVC for move to 4.8% MVC for reach; the levels between micromotions not allowing for complete rest (3.5% MVC). The role of micromotion order and non-micromotion (rest) time will be discussed.

References Laring, et al. (2005). HUM FACTOR ERGON MAN 15(3): 309-325.

4737 Mo, 16:45-17:00 (P12) Prevention of the bad vibration influence on a forklift driver based on vibration measurements Z. Srdjevic 1 , L. Cveticanin 2. 1Faculty of Agriculture, University of Novi Sad, Serbia and Montenegro, 2Faculty of Technical Sciences, University of Novi Sad, Serbia and Montenegro

The paper presents results of the dynamic comfort analysis of the forklift seats. Vibrations (all directions) are measured for the first time on the forklifts man- ufactured by 'Pobeda Holding', Novi Sad, Serbia and Montenegro, although it manufactures forklifts for almost 50 years. Three types of seats were analyzed (Grammer GS12, DPS 200 and prototype forklift seat made in 'Sedista', Priboj, Serbia and Montenegro), mounted on two forklift models: TU20 and DS30. Seats had different suspension systems and different characteristic dimensions. Forklifts were idle running. Mesurements show that there is a small possiblility of occurence of human body resonance on both types of forklifts. But since forklifts' working frequency around 16 Hz, long exposure to vibrations with such frequencies could cause severe health problems. Statistical analysis of the data shows that vibration intensity is highest on the backrest. Measured vibrations on all seats and on both forklift models often exceded allowed exposure values in all directions. After analysis of the measured data, in order to prevent bad vibration influences on forklift driver, it is recommended to manufacturer to mount the prototype forklift seat. Since vibrations on the DS30 model were much intensive then on TU20, it was also recommended to check the leveling of the forklift and the balancing of it's engine. Post measurements on two drivers with significant physical differences, sitting in usual driving position (hands on the weel, legs on the commands), proved that recommendation regarding seat selection was justified.

6282 Mo, 17:00-17:15 (P12) Anatomy-based human models for the simulation of whole-body vibration in jur ies S. RiJtzel, H.E W61fel. Darmstadt University of Technology, Dept. of Structural Dynamics, Darmstadt, Germany

Introduction: Long-term intensive occupational exposure to vibration can cause spinal disorders and affect comfort levels. Existing regulations take the exposure-effect relationship [1] into consideration. However, adequate quantitative data is not yet available to describe these relationships for different seating conditions. Because invasive experiments on humans are limited by ethic concerns, biodynamic models of man have been developed to obtain data which are still lacking. Simulation results of these models will help us to understand the behaviour of the human body and to estimate the consequences of exposure to vibration. Method: The model is based on the anatomical approach. The aim of anatom- ical models is to simulate numerically all quantities potentially relevant for the evaluation of vibration behaviour, as well as to calculate those unknown