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