Is low-cost motion capture with artificial intelligence applicable for human working posture risk assessment during manual material handling? A pilot study

Abstract

BACKGROUND: Assessing working posture risks is important for occupational safety and health. However, low-cost assessment techniques for human motion injuries in the logistics delivery industry have rarely been reported. OBJECTIVE: To propose a novel approach for posture risk assessment using low-cost motion capture with artificial intelligence. METHODS: A Kinect was adopted to obtain red-green-blue (RGB) and depth images of the subject with 24 postures, and the human joints were extracted using artificial intelligence. The images were registered to obtain the actual three-dimensional (3D) human joint angle. RESULTS: The root mean square error (RMSE) significantly decreased. Finally, two common methods for evaluating human working posture injuries—the Rapid Upper Limb Assessment and Ovako Working Posture Analysis System—were investigated. CONCLUSIONS: The outputs of the proposed method are consistent with those of the commercial ergonomic evaluation software.