David A. Rempel

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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(More)
A laboratory study was conducted to determine the effects of work pace on typing force, electromyographic (EMG) activity, and subjective discomfort. We found that as participants typed faster, their typing force and finger flexor and extensor EMG activity increased linearly. There was also an increase in subjective discomfort, with a sharp threshold between(More)
Two studies were conducted to determine the effects of enhanced auditory feedback on typing force, electromyography (EMG) and subjective discomfort. The introduction of enhanced auditory feedback caused a 10-20% reduction in 90th percentile typing force, finger flexor EMG, and finger extensor EMG. Adaptation to the enhanced auditory feedback occurred in <3(More)
A new equation for predicting the hand activity level (HAL) used in the American Conference for Government Industrial Hygienists threshold limit value®(TLV®) was based on exertion frequency (F) and percentage duty cycle (D). The TLV® includes a table for estimating HAL from F and D originating from data in Latko et al. (Latko WA, Armstrong TJ, Foulke JA,(More)
Two computer vision algorithms were developed to automatically estimate exertion time, duty cycle (DC) and hand activity level (HAL) from videos of workers performing 50 industrial tasks. The average DC difference between manual frame-by-frame analysis and the computer vision DC was -5.8% for the Decision Tree (DT) algorithm, and 1.4% for the Feature Vector(More)
An equation was developed for estimating hand activity level (HAL) directly from tracked root mean square (RMS) hand speed (S) and duty cycle (D). Table lookup, equation or marker-less video tracking can estimate HAL from motion/exertion frequency (F) and D. Since automatically estimating F is sometimes complex, HAL may be more readily assessed using S.(More)
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