A Single-Camera Computer Vision-Based Method for 3D L5/S1 MomentEstimation During Lifting Tasks

Abstract

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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