Vision-based Ergonomic and Fatigue Analyses for Advanced Manufacturing

Manuela Kunz, Chang Shu, Michel Picard, Daniel Vera, Peter Hopkinson, Pengcheng Xi

Abstract: In advanced manufacturing, ergonomic risk assess-ment and fatigue analysis ensure not only the health of human operators, but also the productivity and quality of the manu-facturing process. In this paper we investigate a vision-based method for automatic ergonomic and fatigue risk monitoring, based on a cost-efficient 3D camera system and AI-driven video-based approaches for 3D body posture analysis and repetition counting. Our laboratory trials showed that this method was able to track joint motions with an average accuracy of 3.5°, performed comparable to a human operator when assessing the ergonomic risk, and was able to track and focus on repetitive motions of the human operator. The proposed method supports data visualization, real-time ergonomic and fatigue analyses, and report generation, and has the potential to support the development of better manufacturing environments.