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%.

A Conditional Variational Auto-encoder Model for Reducing Musculoskeletal Disorder Risk during a Human-Robot Collaboration Task

“…Second, we admit that the reconstructed body pose through VideoPose3D, which is used to calculate the reference point of operation position, may carry errors. A previous study showed that the joint location error due to computer-vision algorithm is around 7.7% (Wang et al, 2021). For a more precise validation, a laboratory- grade motion tracking system would be needed.…”

“…In the study conducted by Martinez (2017), human motion was well reconstructed using a simple and lightweight deep neural network. The fast-developing machine learning methods appear to be capable to form a more efficient way of understanding and improving workers’ postures (Li, et al, 2020; Wang et al, 2021). However, computer vision-aided posture assessment heavily relies on the workplace configuration and hardware.…”