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Modeling and Analysis of Human Comfort in Human–Robot Collaboration

The emergence and recent development of collaborative robots have introduced a safer and more efficient human–robot collaboration (HRC) manufacturing environment. Since the release of COBOTs, a great amount of research efforts have been focused on improving robot working efficiency, user safety, hum...

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Detalles Bibliográficos
Autores principales: Yan, Yuchen, Su, Haotian, Jia, Yunyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10604725/
https://www.ncbi.nlm.nih.gov/pubmed/37887595
http://dx.doi.org/10.3390/biomimetics8060464
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author Yan, Yuchen
Su, Haotian
Jia, Yunyi
author_facet Yan, Yuchen
Su, Haotian
Jia, Yunyi
author_sort Yan, Yuchen
collection PubMed
description The emergence and recent development of collaborative robots have introduced a safer and more efficient human–robot collaboration (HRC) manufacturing environment. Since the release of COBOTs, a great amount of research efforts have been focused on improving robot working efficiency, user safety, human intention detection, etc., while one significant factor—human comfort—has been frequently ignored. The comfort factor is critical to COBOT users due to its great impact on user acceptance. In previous studies, there is a lack of a mathematical-model-based approach to quantitatively describe and predict human comfort in HRC scenarios. Also, few studies have discussed the cases when multiple comfort factors take effect simultaneously. In this study, a multi-linear-regression-based general human comfort prediction model is proposed under human–robot collaboration scenarios, which is able to accurately predict the comfort levels of humans in multi-factor situations. The proposed method in this paper tackled these two gaps at the same time and also demonstrated the effectiveness of the approach with its high prediction accuracy. The overall average accuracy among all participants is 81.33%, while the overall maximum value is 88.94%, and the overall minimum value is 72.53%. The model uses subjective comfort rating feedback from human subjects as training and testing data. Experiments have been implemented, and the final results proved the effectiveness of the proposed approach in identifying human comfort levels in HRC.
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spelling pubmed-106047252023-10-28 Modeling and Analysis of Human Comfort in Human–Robot Collaboration Yan, Yuchen Su, Haotian Jia, Yunyi Biomimetics (Basel) Article The emergence and recent development of collaborative robots have introduced a safer and more efficient human–robot collaboration (HRC) manufacturing environment. Since the release of COBOTs, a great amount of research efforts have been focused on improving robot working efficiency, user safety, human intention detection, etc., while one significant factor—human comfort—has been frequently ignored. The comfort factor is critical to COBOT users due to its great impact on user acceptance. In previous studies, there is a lack of a mathematical-model-based approach to quantitatively describe and predict human comfort in HRC scenarios. Also, few studies have discussed the cases when multiple comfort factors take effect simultaneously. In this study, a multi-linear-regression-based general human comfort prediction model is proposed under human–robot collaboration scenarios, which is able to accurately predict the comfort levels of humans in multi-factor situations. The proposed method in this paper tackled these two gaps at the same time and also demonstrated the effectiveness of the approach with its high prediction accuracy. The overall average accuracy among all participants is 81.33%, while the overall maximum value is 88.94%, and the overall minimum value is 72.53%. The model uses subjective comfort rating feedback from human subjects as training and testing data. Experiments have been implemented, and the final results proved the effectiveness of the proposed approach in identifying human comfort levels in HRC. MDPI 2023-10-01 /pmc/articles/PMC10604725/ /pubmed/37887595 http://dx.doi.org/10.3390/biomimetics8060464 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Yan, Yuchen
Su, Haotian
Jia, Yunyi
Modeling and Analysis of Human Comfort in Human–Robot Collaboration
title Modeling and Analysis of Human Comfort in Human–Robot Collaboration
title_full Modeling and Analysis of Human Comfort in Human–Robot Collaboration
title_fullStr Modeling and Analysis of Human Comfort in Human–Robot Collaboration
title_full_unstemmed Modeling and Analysis of Human Comfort in Human–Robot Collaboration
title_short Modeling and Analysis of Human Comfort in Human–Robot Collaboration
title_sort modeling and analysis of human comfort in human–robot collaboration
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10604725/
https://www.ncbi.nlm.nih.gov/pubmed/37887595
http://dx.doi.org/10.3390/biomimetics8060464
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