Automated video exposure assessment of repetitive hand activity level for a load transfer task.

OBJECTIVE: A new method is described for automatically quantifying repetitive hand activity with the use of digital video processing.
BACKGROUND: The hand activity level (HAL) is widely used for evaluating repetitive hand work. Conventional methods involving either a trained observer on- or off-site or manual off-site video analysis are often considered inaccurate, cumbersome, or impractical for routine work assessment
METHOD: A cross-correlation-based template-matching algorithm was programmed to track the motion trajectory of a selected region of interest across successive video frames for a single camera to measure repetition frequency, duty cycle, and HAL. A simple, paced, load transfer task was used to simulate a repetitive industrial activity. A total of 12 participants were videoed performing the task for varying HAL conditions. The automatically predicted HAL was compared with the manually measured HAL with the use of frame-by-frame video analysis.
RESULTS: Predicted frequency, duty cycle, and HAL were in concert with the manually measured HAL conditions. The linear regression slopes of the automatically predicted values with respect to the manually measured values were 0.98 (R2 = .79), 1.27 (R2 = .63), and 1.06 (R2 = .77) for frequency, duty cycle, and HAL, respectively.
CONCLUSION: A proof-of-concept for automatic video-based direct exposure assessment was demonstrated. APPLICATION: The video assessment method for repetitive motion is promising for automatic, unobtrusive, and objective exposure assessment, which may offer broad availability with the use of a camera-enabled mobile device for helping evaluate, prevent, and control exposure to repetitive motions related to upper-extremity injuries in the workplace.