| DOI | Trouver le DOI : https://doi.org/10.1109/ICPS51978.2022.9816946 |
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| Auteur | Rechercher : Kunz, Manuela1; Rechercher : Shu, Chang1; Rechercher : Picard, Michel1; Rechercher : Vera, Daniel; Rechercher : Hopkinson, Peter; Rechercher : Xi, Pengcheng1 |
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| Affiliation | - Conseil national de recherches du Canada. Technologies numériques
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| Format | Texte, Article |
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| Conférence | 2022 IEEE 5th International Conference on Industrial Cyber-Physical Systems (ICPS), May 24-26, 2022, Coventry, United Kingdom (Online Conference) |
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| Description physique | 7 p. |
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| Sujet | computer vision; ergonomics; fatigue; advanced manufacturing; machine learning; 3D skeleton tracking |
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| Résumé | 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. |
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| Date de publication | 2022-07-18 |
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| Maison d’édition | IEEE |
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| Dans | |
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| Langue | anglais |
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| Publications évaluées par des pairs | Oui |
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| Exporter la notice | Exporter en format RIS |
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| Signaler une correction | Signaler une correction (s'ouvre dans un nouvel onglet) |
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| Identificateur de l’enregistrement | 2776e9b8-befa-474e-8565-151b1a4597c6 |
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| Enregistrement créé | 2022-09-09 |
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| Enregistrement modifié | 2022-09-14 |
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