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Excessive low back joint loading during material handling tasks is considered a critical risk factor of musculoskeletal disorders (MSD). Therefore, it is necessary to understand the low-back joint loading during manual material handling to prevent low-back injuries. Recently, computer vision-based pose reconstruction methods have shown the potential in human kinematics and kinetics analysis. This study performed L5/S1 joint moment estimation by combining VideoPose3D, an open-source pose reconstruction library, and a biomechanical model. Twelve participants lifting a 10 kg plastic crate from the floor to a knuckle-height shelf were captured by a camera and a laboratory-based motion tracking system. The L5/S1 joint moments obtained from the camera video were compared with those obtained from the motion tracking system. The comparison results indicate that estimated total peak L5/S1 moments during lifting tasks were positively correlated to the reference L5/S1 joint moment, and the percentage error is 7.7%.
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This content will become publicly available on August 25, 2026
Toward Full Automation of the Revised NIOSH Lifting Equation
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.
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- Award ID(s):
- 2013451
- PAR ID:
- 10649346
- Publisher / Repository:
- SAGE Publications
- Date Published:
- Journal Name:
- Proceedings of the Human Factors and Ergonomics Society Annual Meeting
- Volume:
- 69
- Issue:
- 1
- ISSN:
- 1071-1813
- Format(s):
- Medium: X Size: p. 1379-1381
- Size(s):
- p. 1379-1381
- Sponsoring Org:
- National Science Foundation
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