Bernard J. Martin

Analysis of the tonic vibration reflex: Influence of vibration variables on motor unit synchronization and fatigue

Abstract The influence of vibration frequency (40, 80, 100, 120, 150, or 200 Hz) at selected displacement amplitudes (0.2, 0.3 mm) on tonic vibration reflex (TVR) characteristics was investigated. The degree of synchronization of motor unit activity with vibratory stimuli in ten humans was determined using the electromyographic (EMG) activity of the finger and wrist flexor muscles when vibration was applied to the distal tendons of the hand flexor muscles. The EMG spectral analysis indicates that harmonic and subharmonic motor unit synchronization mechanisms contribute to the modulation of the amplitude of the TVR as the vibration frequency increases. Harmonic synchronization decreases while subharmonic synchronization increases as vibration frequency increases. It is suggested that the synchronization process influences muscle fatigue, since it forces the driving of motor units, leading to a decrease in contraction efficiency. This phenomenon most probably results from an impairment of excitation-contraction coupling. High-frequency vibration (>150 Hz) tends to induce less motor unit synchronization in a frequency range beyond the known mechanical resonance of biological tissues. The findings of this study may be applied to the design of hand-held power tools, since their vibration triggers the TVR in active muscles.

AGE-RELATED DIFFERENCES IN UPPER LIMB PROPRIOCEPTIVE ACUITY '.'

Although upper limb movements are known to be slower and more variable in elderly persons, the extent to which these changes are associated with deficits in movement-related sensory feedback is poorly understood, despite the importance of proprioception in the control of skilled movement. Age-related changes were examined with 22 participants (10 of M age 27 years and 12 of M age 75 years) in performance of an elbow position-matching task which varied in terms of interhemispheric transfer and/or the need to retrieve memory-based proprioceptive information. Matching errors were significantly greater, and movements more prolonged, and irregular in their time course in the elderly group than in the young group. Impaired performance in conditions requiring interhemispheric transfer and retrieval of memory-based proprioceptive information reflected the importance of cognitive processing during complex sensorimotor tasks. This novel matching paradigm provided a sensitive means of manipulating the demands of the task and may be an effective method for as sessing both cognitive and sensorimotor declines associated with aging.

Keyboard reaction force and finger flexor electromyograms during computer keyboard work

This study examines the relationship between forearm EMGs and keyboard reaction forces in 10 people during keyboard tasks performed at a comfortable speed. A linear fit of EMG force data for each person and finger was calculated during static fingertip loading. An average r2 of .71 was observed for forces below 50% of the maximal voluntary contraction (MVC). These regressions were used to characterize EMG data in force units during the typing task. Averaged peak reaction forces measured during typing ranged from 3.33 N (thumb) to 1.84 N (little finger), with an overall average of 2.54 N, which represents about 10% MVC and 5.4 times the key switch make force (0.47 N). Individual peak or mean finger forces obtained from EMG were greater (1.2 to 3.2 times) than force measurements; hence the range of r2 for EMG force was .10 to .46. A closer correspondence between EMG and peak force was obtained using EMG averaged across all fingers. For 5 of the participants the force computed from EMG was within ±20% of the reaction force. For the other 5 participants forces were overestimated. For 9 participants the difference between EMG estimated force and the reaction force was less than 13% MVC. It is suggested that the difference between EMG and finger force partly results from the amount of muscle load not captured by the measured applied force.

The Effects of Keyswitch Stiffness on Typing Force, Finger Electromyography, and Subjective Discomfort

The effects of keyswitch stiffness and key action on typing force, electromyography (EMG), and subjective preference were examined. Each subjects own keyboard (with an audible key click and key activation force of 0.72 N) and three keyboards with no key click that were identical in design but had different key activation forces (0.28 N, 0.56 N, and 0.83 N) were used. Subjects (24 female transcriptionists) typed on each keyboard for 15 min while typing force and left hand surface EMG of the finger flexor and extensor muscles were monitored. Subjects then used one of the keyboards at their workstations for 7 workdays and were monitored again. This procedure was repeated for all four keyboards. Typing force and finger flexor and extensor EMG activity were highest for the 0.83 N keyboard. Lowest EMG values were for the 0.28 N and the 0.72 N audible key click keyboards. Baseline (10th percentile) and median (50th percentile) extensor EMG values were significantly higher than flexor EMG values. Peak (90th percentile) EMG values were comparable for flexors and extensors. Mean subjective discomfort was significantly higher for the 0.83 N keyboard at the fingers (36% higher), lower arm (40% higher), and overall (39% higher). Seventeen of 24 subjects preferred the 0.72 N keyboard, 4 the 0.28 N keyboard, and 3 preferred the 0.56 N keyboard. Results suggest that increasing make force causes typing force and EMG to increase but that the ratio of 90th centile typing force to make force decreases as make force increases. Subjective discomfort was significantly higher for the keyboard with 0.83 N make force. Buckling spring keyboards have better feedback characteristics, which may be responsible for a decrease in the amount of typing force and EMG produced.

Muscle responses to simulated torque reactions of hand-held power tools

The aim of this work was to investigate physiological responses to torque reaction forces produced by hand-held power tools used to tighten threaded fasteners. Such tools are used repetitively by workers in many industries and are often associated with upper limb musculoskeletal complaints. The tools considered for stimulation in this study had straight handles and required from 100 to 400 ms to tighten fasteners to a peak torque of 1.0 to 2.5 Nm and from 50 to 150 ms for the torque to decay to zero. A tool stimulator was constructed to apply a programmed torque profile to a handle similar to that of a straight in-line power screwdriver. Wrist flexor and extensor surface EMGs and handle position were recorded as subjects held handles subjected to controlled torque loads that tended to flex the wrist. It was found that: (1) very high EMG values occurred even though torques were of short duration (50 to 600 ms) and the peak torques were low (7-28% of maximum strength); (2) high EMGs in anticipation of torque are directly related to torque build-up rate and peak torque; (3) high peak flexor and extensor EMGs during and following torque onset are related to torque build-up rate and peak torque; (4) minimum time of peak EMGs of 72-87 ms following the onset of torques with 50 ms build-up suggests the contribution of an extensor muscle stretch reflex component; delayed peak for longer build-ups suggests a central control of muscle force in response to torque; (5) angular excursions of handles increase with decreasing torque build-up time and increasing torque magnitude causes increasing eccentric work; (6) the results show that the slow torque build-up times (450 ms) correspond to minimum peak EMGs; and (7) accumulated EMGs increase with increasing torque and torque build-up times. Further studies are needed to evaluate fatigue and musculoskeletal injuries associated with prolonged periods of tool use.

The relationship between shoulder torques and the perception of muscular effort in loaded reaches

The objective of this study was to define the quantitative relationship between external dynamic shoulder torques and calibrated perceived muscular effort levels for load delivery tasks, for application in job analyses. Subjects performed a series of loaded reaches and, following each exertion, rated their perceived shoulder muscular effort. Motion and task physical requirements data were processed with a biomechanical upper extremity model to calculate external dynamic shoulder torques. Calculated torque values were then statistically compared to reported calibrated perceived muscular effort scores. Individual subject torque profiles were significantly positively correlated with perceived effort scores (r2 = 0.45–0.77), with good population agreement (r2 = 0.50). The accuracy of the general regression model improved (r2 = 0.72) with inclusion of factors specific to task geometry and individual subjects. This suggests two major conclusions: 1) that the perception of muscular shoulder effort integrates several factors and this interplay should be considered when evaluating tasks for their impact on the shoulder region; 2) the torque/perception relationship may be usefully leveraged in job design and analysis.

The HUMOSIM Ergonomics Framework: A New Approach to Digital Human Simulation for Ergonomic Analysis

The potential of digital human modeling to improve the design of products and workspaces has been limited by the time-consuming manual manipulation of figures that is required to perform simulations. Moreover, the inaccuracies in posture and motion that result from manual procedures compromise the fidelity of the resulting analyses. This paper presents a new approach to the control of human figure models and the analysis of simulated tasks. The new methods are embodied in an algorithmic framework developed in the Human Motion Simulation (HUMOSIM) laboratory at the University of Michigan. The framework consists of an interconnected, hierarchical set of posture and motion modules that control aspects of human behavior, such as gaze or upper-extremity motion. Analysis modules, addressing issues such as shoulder stress and balance, are integrated into the framework. The framework encompasses many individual innovations in motion simulation algorithms, but the primary innovation is in the development of a comprehensive system for motion simulation and ergonomic analysis that is specifically designed to be independent of any particular human modeling system. The modules are developed as lightweight algorithms based on closed-form equations and simple numerical methods that can be communicated in written form and implemented in any computer language. The modules are independent of any particular figure model structure, requiring only basic forward-kinematics control and public-domain numerical algorithms. Key aspects of the module algorithms are “behavior-based,” meaning that the large amount of redundancy in the human kinematic linkage is resolved using empirical models based on laboratory data. The implementation of the HUMOSIM framework in human figure models will allow much faster and more accurate simulation of human interactions with products and workspaces using high-level, task-based control. INTRODUCTION Digital human figure models (DHM) are now widely used for ergonomic analysis of products and workplaces. In many organizations, DHM software is a tool of first resort for answering questions relating to physical interaction between people and objects. Yet any objective appraisal of the technology would conclude that the current reality of DHM software capability is far from the promise of a “digital human” that can interact realistically with products and environments. This paper is focused on efforts to improve the ability of DHM software to simulate physical posture and motion. Nearly every other aspect of DHM functionality also warrants improvement, including body shape representation, strength simulation, and cognitive function, but posture and motion are critical to the primary applications of DHM to the assessment of physical tasks. Posture simulation is as old as computerized manikins, because the manikin must be postured before an analysis can be conducted. Important early work was performed by Ryan for the U.S. Navy (Ryan 1970). Porter et al. (1993) summarized applications of digital human models in vehicle ergonomics during the early years of personal computers, at which time few of the current commercial DHM software tools were in use. Chaffin (2001) presented case studies of the expanding use of DHM for both product and workplace design and assessment. As evidence of the importance of posture and motion simulation, dozens of papers in the SAE literature and in other forums have presented a wide variety of methods for human simulating postures and motions, including multiple-regression (Snyder et al. 1972); analytic and numerical inverse kinematics (Jung et al. 1995; Tolani et al. 2000); optimization-based inverse kinematics (Wang and Verriest 1998); differential inverse kinematics (Zhang and Chaffin, 2000); functional regression on stretch-pivot parameters (Faraway 2000); scaling, warping, and blending of motion-capture data (Park et al. 2002; Faraway 2003; Monnier et al. 2003; Park et al. 2004; Dufour and Wang 2005); and many 2006-01-2365 The HUMOSIM Ergonomics Framework: A New Approach to Digital Human Simulation for Ergonomic Analysis Matthew P. Reed, Julian Faraway, Don B. Chaffin and Bernard J. Martin University of Michigan

Effects of Key Stiffness on Force and the Development of Fatigue While Typing

An experiment was conducted to investigate the effect of key stiffness on the development of fatigue, keyboard reaction forces, and muscle electromyography (EMG) responses. Six subjects typed continuously for 2 hours on each of two keyboards (0.28 N or 0.83 N resistance keys, presented in random order). Keyboard reaction force and root mean square finger flexor and extensor EMG were recorded for 2 minutes at 250 Hz for every 10 minutes subjects typed. After typing for 2 hours subjects were given a 2-hour rest break and then typed on the remaining keyboard for an additional 2 hours Fifty-four percent more peak force, 34% more peak finger flexor EMG, and 2% more peak finger extensor EMG were exerted while using the 0.83 N keyboard. Peak and 90th percentile values showed similar trends and were well correlated for force and finger flexor and extensor EMG. Subjects typed much harder than necessary (4.1 to 7.0 times harder on the 0.28 N keyboard and 2.2 to 3.5 times harder on the 0.83 N keyboard) to activate the keys. Fatigue was observed on the 0.83 N keyboard during 2 hours of continuous typing, but the trends were mild. It appears that the ratio of typing force to flexor EMG may not be a sensitive enough indicator of fatigue for low-force high repetition work.

Predictors of perceived effort in the shoulder during load transfer tasks

The mechanism of muscular effort perception in the shoulder was examined in this experiment. Two shoulder biomechanical models and experimental muscle activity data were used to assess physical exposure for a series of reaching tasks. Effort perception was quantitatively correlated to these measures of physical loading, both at the resultant torque (r2 = 0.50) and muscle activity model-based muscle force predictions (MFPs): r2 = 0.42, electromyography (EMG): r2 = 0.26) levels. Muscle data did not explain variation in effort perception more fully than torque data. The inclusion of subject and task variables improved the ability of each model to explain variability in effort perception (torque: r2 = 0.74; MFP: r2 = 0.67, EMG: r2 = 0.64). These results suggest that effort perception may not be fully explained by only an image of the motor command, but is rather a complex integrative quantity that is affected by other factors, such as posture and task goals, which may be dependent on sensory feedback.

Eye-hand coordination of symmetric bimanual reaching tasks: temporal aspects

Both synchronous and asynchronous coordination modes have been evidenced in bimanual movements, but psychology and motor control literatures seem to be inconclusive about what factors specifically drive these modes and when one is preferred over the other. The goal of the present study was to determine the relationship between visual feedback and the temporal symmetry/asymmetry of the two hand movements in symmetric bimanual reach movements by a systematic analysis of eye movements and their role in coordination. The coupling/decoupling of hand movements caused by the competing visual demands of each task was analyzed in a bimanual experimental paradigm in which the objects to be transported, tolerances of the placement targets, and inter-target distance were varied. The results show that although temporally symmetric until peak velocity, the extent of synchrony during the terminal phases of hand movements was significantly influenced by the visual demand associated with the experimental conditions. Four distinct eye–hand coordination patterns were identified, based on sequencing of hand movements and timing of gaze shifts from one target to another. These patterns significantly affected the kinematics of hand movements and the degree of temporal synchrony in terminal phases, thus stressing the importance of a rigorous analysis of eye movements in understanding the mechanisms of eye–hand coordination. When faced with competing visual demands, left hand guidance required more foveal visual information of the target, while right hand control could proceed until the terminal stages with the target in the peripheral field of view, thus indicating an asymmetry in the feedback requirements of the two hand systems when accuracy is critical.