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Is low-cost motion capture with artificial intelligence applicable for human working posture risk assessment during
Renjie Zhang1,2, Jianwei Niu1, Linghua Ran3
1School of Mechanical Engineering, University of Science and Technology Beijing, Beijing, China.
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.
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