An Equation for Estimating Hand Activity Level Based on Measured Hand Speed and Duty Cycle. (September 2014)

• Record Type: Journal Article
• Authors: Akas, Oguz; Azari, David; Chen, Chia-Hsiung (Eric); Hu, Yu Hen; Armstrong, Thomas J.; Ulin, Sheryl S.; Radwin, Robert G.
• Is Part Of: Proceedings of the Human Factors and Ergonomics Society ... Annual Meeting. Volume 58:Part 1(2014)
• Page Start: 1600
• Page End: 1604
• Publication Date: 2014-09
• Subjects: Human engineering -- Congresses; 620.8205
• DOI: 10.1177/1541931214581333
• Languages: English
• ISSNs: 1541-9312

Abstract

We are developing video processing algorithms for automatically measuring the ACGIH TLV® hand activity level (HAL) using marker-less tracking of hand movements. An equation for computing HAL ratings directly from tracked kinematics, rather than using a frequency-duty cycle (DC) look-up table, more readily lends itself to automated processing. Videos from the 33 Latko et al. (1997) jobs were digitized and analyzed using marker-less tracking, and hand root mean square (RMS) speed (S) was measured. A linear regression model was developed for predicting the average observer rated HAL based on the measured hand RMS speed and DC. Since the videos did not contain distance calibration, speed was quantified in pixels/s and normalized by the distance of each worker's hand breadth, measured in pixels. A Monte Carlo simulation was performed using the US Army (1991) hand anthropometry data to determine how variation is introduced in the equation as hand breadth varies. The resulting equation was HAL= −1.06 + 0.0047 S + 0.053 DC and it predicted HAL ratings within ±1. The development of an accurate equation for estimating HAL ratings should enable use of automated and objective measurement in practice. While expert observer HAL ratings offer speed and efficiency, use of objective measurements based on worker hand kinematics should provide greater reliability, as well as offering specific engineering aspects of the job that may be addressed for reducing exposures and the risk of musculoskeletal disorders. Furthermore, automated videos analysis may help improve the speed and efficiency of making objective measurements in practice.