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

Article type: Research Article

Authors: Zhang, Renjie^{a; b} | Niu, Jianwei^{a; *} | Ran, Linghua^c

Affiliations: [a] School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China | [b] School of Mechanical Engineering and Automation, Beihang University, Beijing, China | [c] China National Institute of Standardization, Beijing, China

Correspondence: [*] Address for correspondence: Jianwei Niu, School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China. E-mail: [email protected].

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.

Keywords: Posture risk assessment, low-cost motion capture, artificial intelligence, Rapid Upper Limb Assessment (RULA), Ovako Working Posture Analysis System (OWAS)

DOI: 10.3233/WOR-205204

Journal: Work, vol. 74, no. 1, pp. 283-293, 2023