This exploratory research investigates the feasibility of automatically evaluating the American Conference of Government Industrial Hygienists (ACGIH) Hand Activity Level (HAL) using digital video processing. We are developing feature extraction algorithms for quantifying HAL in real-time using conventional digital videos focused on the upper extremities. This research is significant because there is currently no practical instrument for objectively, unobtrusively, and efficiently measuring repetitive motion exposure for evaluating the risk of musculoskeletal injuries in the workplace. Current methods involve either direct measurements using instruments attached to a worker's hands or arms, or indirect observations. Both instrument and observation methods are mostly limited to research studies and are highly impractical for industry practitioners. The proposed approach is innovative because it uses digital video processing to measure repetitive motion exposure and because it leverages a vast data base of videos and associated exposure data already analyzed manually through collaboration with the University of California-Berkeley (UCB). UCB is making available 320 videos of workers in four industries, and their associated, analyses for this initial study. A proof-of-concept algorithm has already been tested. We first propose to further develop and refine video processing algorithms for automatically and continuously measuring HAL from standard digital video recordings focused on the upper extremities. The video algorithms will then be evaluated by comparing repetition frequency, duty cycle and HAL derived by our algorithm from video clips of laboratory subjects performing paced repetitive tasks or actual industrial workers, against similar outcomes obtained by a human analyst conducting a manual frame-by-frame multimedia video task analysis. Ultimately this translational research might lead to a video-based direct reading exposure assessment instrument with broad applications for occupational health and safety.
Upper extremity musculoskeletal injuries are common in hand intensive work involving highly repetitive motions and exertions. This exploratory research will develop video processing algorithms to quantify hand activity level in real-time using conventional digital videos focused on the upper extremities for primary prevention and control of work related disorders.
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