| DOI | Resolve DOI: https://doi.org/10.1109/ICPS51978.2022.9816946 |
|---|
| Author | Search for: Kunz, Manuela1; Search for: Shu, Chang1; Search for: Picard, Michel1; Search for: Vera, Daniel; Search for: Hopkinson, Peter; Search for: Xi, Pengcheng1 |
|---|
| Affiliation | - National Research Council of Canada. Digital Technologies
|
|---|
| Format | Text, Article |
|---|
| Conference | 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS), May 24-26, 2022, Coventry, United Kingdom (Online Conference) |
|---|
| Physical description | 7 p. |
|---|
| Subject | computer vision; ergonomics; fatigue; advanced manufacturing; machine learning; 3D skeleton tracking |
|---|
| 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. |
|---|
| Publication date | 2022-07-18 |
|---|
| Publisher | IEEE |
|---|
| In | |
|---|
| Language | English |
|---|
| Peer reviewed | Yes |
|---|
| Export citation | Export as RIS |
|---|
| Report a correction | Report a correction (opens in a new tab) |
|---|
| Record identifier | 2776e9b8-befa-474e-8565-151b1a4597c6 |
|---|
| Record created | 2022-09-09 |
|---|
| Record modified | 2022-09-14 |
|---|
