Toward Full Automation of the Revised NIOSH Lifting Equation

Abstract

Workers performing repetitive lifting tasks are at high risk of developing low-back work-related musculoskeletal disorders. While the Revised NIOSH Lifting Equation (RNLE) is a widely used tool for evaluating lifting-related risks, its reliance on manual measurement limits its scalability and efficiency. This study proposes a computer vision-based framework that automates RNLE computation using video data. The method integrates three key stages: (1) pose estimation to extract 3D joint coordinates, (2) lifting action recognition via kinematic features and a k-TSP classifier, and (3) estimation of RNLE multipliers from joint data. Applied to 40 lifting trials with motion capture-based ground-truth, the system achieved a coefficient of determination of 0.82 and a mean absolute error of 2.72 kg in estimating recommended weight limits. These findings demonstrate the potential of computer vision to automate ergonomic risk assessments. Future work will aim to expand task diversity and integrate coupling assessment for full RNLE coverage.