{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T21:10:28Z","timestamp":1777669828920,"version":"3.51.4"},"reference-count":49,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2024,5,22]],"date-time":"2024-05-22T00:00:00Z","timestamp":1716336000000},"content-version":"vor","delay-in-days":366,"URL":"http:\/\/www.sagepub.com\/licence-information-for-chorus"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2024688"],"award-info":[{"award-number":["2024688"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Hum Factors"],"published-print":{"date-parts":[[2024,6]]},"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>This study aims to improve workers\u2019 postures and thus reduce the risk of musculoskeletal disorders in human-robot collaboration by developing a novel model-free reinforcement learning method.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Human-robot collaboration has been a flourishing work configuration in recent years. Yet, it could lead to work-related musculoskeletal disorders if the collaborative tasks result in awkward postures for workers.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>The proposed approach follows two steps: first, a 3D human skeleton reconstruction method was adopted to calculate workers\u2019 continuous awkward posture (CAP) score; second, an online gradient-based reinforcement learning algorithm was designed to dynamically improve workers\u2019 CAP score by adjusting the positions and orientations of the robot end effector.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>In an empirical experiment, the proposed approach can significantly improve the CAP scores of the participants during a human-robot collaboration task when compared with the scenarios where robot and participants worked together at a fixed position or at the individual elbow height. The questionnaire outcomes also showed that the working posture resulted from the proposed approach was preferred by the participants.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The proposed model-free reinforcement learning method can learn the optimal worker postures without the need for specific biomechanical models. The data-driven nature of this method can make it adaptive to provide personalized optimal work posture.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Application<\/jats:title>\n                    <jats:p>The proposed method can be applied to improve the occupational safety in robot-implemented factories. Specifically, the personalized robot working positions and orientations can proactively reduce exposure to awkward postures that increase the risk of musculoskeletal disorders. The algorithm can also reactively protect workers by reducing the workload in specific joints.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/00187208231177574","type":"journal-article","created":{"date-parts":[[2023,5,22]],"date-time":"2023-05-22T22:13:16Z","timestamp":1684793596000},"page":"1754-1769","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":12,"title":["Improving Workers\u2019 Musculoskeletal Health During Human-Robot Collaboration Through Reinforcement Learning"],"prefix":"10.1177","volume":"66","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1160-3822","authenticated-orcid":false,"given":"Ziyang","family":"Xie","sequence":"first","affiliation":[{"name":"North Carolina State University, Raleigh, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lu","family":"Lu","sequence":"additional","affiliation":[{"name":"North Carolina State University, Raleigh, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanwen","family":"Wang","sequence":"additional","affiliation":[{"name":"North Carolina State University, Raleigh, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bingyi","family":"Su","sequence":"additional","affiliation":[{"name":"North Carolina State University, Raleigh, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yunan","family":"Liu","sequence":"additional","affiliation":[{"name":"North Carolina State University, Raleigh, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8790-3103","authenticated-orcid":false,"given":"Xu","family":"Xu","sequence":"additional","affiliation":[{"name":"North Carolina State University, Raleigh, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2023,5,22]]},"reference":[{"key":"bibr52-00187208231177574","volume-title":"Psychobiology of physical activity","author":"Acevedo E. O.","year":"2006","edition":"1"},{"key":"bibr2-00187208231177574","unstructured":"Anita A. R., Yazdani A., Hayati K. S., Adon M. Y. (2014). Association between awkward posture and musculoskeletal disorders (MSD) among assembly line workers in an automotive industry."},{"key":"bibr3-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2020.102022"},{"key":"bibr4-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-35289-8_25"},{"key":"bibr5-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1109\/IROS.2017.8206107"},{"key":"bibr6-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA.2018.8462927"},{"key":"bibr7-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1109\/ACC.2012.6315022"},{"issue":"7","key":"bibr8-00187208231177574","volume":"12","author":"Duchi J.","year":"2011","journal-title":"Journal of Machine Learning Research"},{"key":"bibr9-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1016\/j.ergon.2012.09.004"},{"key":"bibr10-00187208231177574","unstructured":"Freivalds A., Niebel B. W. (2008). Niebel\u2019s Methods, Standards, and Work Design."},{"key":"bibr12-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2020.101998"},{"key":"bibr13-00187208231177574","doi-asserted-by":"crossref","unstructured":"Haarnoja T., Ha S., Zhou A., Tan J., Tucker G., Levine S. (2018a). Learning to walk via deep reinforcement learning.\n                      ArXiv Preprint ArXiv:1812.11103\n                      .","DOI":"10.15607\/RSS.2019.XV.011"},{"key":"bibr14-00187208231177574","doi-asserted-by":"crossref","unstructured":"Haarnoja T., Pong V., Zhou A., Dalal M., Abbeel P., Levine S. (2018b). Composable deep reinforcement learning for robotic manipulation. 2018 IEEE International Conference on Robotics and Automation (ICRA), Brisbane, QLD, Australia, 21-25 May 2018, pp. 6244\u20136251.","DOI":"10.1109\/ICRA.2018.8460756"},{"key":"bibr15-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1016\/S0003-6870(99)00039-3"},{"key":"bibr16-00187208231177574","doi-asserted-by":"crossref","unstructured":"Hu B., Shao S., Cao Z., Xiao Q., Li Q., Ma C. (2019). Learning a faster locomotion gait for a quadruped robot with model-free deep reinforcement learning. 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO), pp. 1097\u20131102.","DOI":"10.1109\/ROBIO49542.2019.8961651"},{"key":"bibr17-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1016\/j.proeng.2011.11.142"},{"key":"bibr18-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1080\/00140138608967233"},{"issue":"10","key":"bibr19-00187208231177574","first-page":"1","volume":"26","author":"Kang D.","year":"2014","journal-title":"Annals of Occupational and Environmental Medicine"},{"issue":"2","key":"bibr20-00187208231177574","doi-asserted-by":"crossref","first-page":"285","DOI":"10.1080\/10803548.2019.1710933","volume":"26","author":"Kee D.","year":"2020","journal-title":"International journal of occupational safety and ergonomics: JOSE"},{"key":"bibr21-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1016\/j.ergon.2021.103140"},{"key":"bibr22-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1016\/0169-8141(92)90062-5"},{"key":"bibr23-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1186\/s13099-017-0218-5"},{"key":"bibr24-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1177\/0278364913495721"},{"key":"bibr25-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1177\/1071181319631174"},{"key":"bibr26-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00445"},{"key":"bibr27-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1016\/j.promfg.2020.10.026"},{"key":"bibr28-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1016\/j.apergo.2017.02.015"},{"key":"bibr29-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1016\/j.cie.2020.106816"},{"key":"bibr30-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1016\/0003-6870(93)90080-S"},{"key":"bibr32-00187208231177574","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph16050793"},{"key":"bibr33-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1080\/00140130903311617"},{"key":"bibr34-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2019.00794"},{"key":"bibr35-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-019-04638-6"},{"key":"bibr36-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1109\/HUMANOIDS.2017.8239537"},{"key":"bibr37-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1007\/s10514-017-9678-1"},{"issue":"1","key":"bibr38-00187208231177574","first-page":"109","volume":"39","author":"Qu X.","year":"2008","journal-title":"IEEE Transactions On Systems, Man, And Cybernetics-Part A: Systems And Humans"},{"key":"bibr39-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1016\/j.rcim.2018.12.015"},{"key":"bibr40-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1007\/s10846-020-01183-3"},{"key":"bibr41-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0072703"},{"key":"bibr42-00187208231177574","volume":"25","author":"Snoek J.","year":"2012","journal-title":"Advances in Neural Information Processing Systems"},{"key":"bibr43-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1080\/00140139108964855"},{"key":"bibr44-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1002\/9781118814239"},{"key":"bibr45-00187208231177574","volume-title":"Reinforcement learning: An introduction","author":"Sutton R. S.","year":"2018"},{"key":"bibr46-00187208231177574","volume-title":"Occupational injuries and illnesses resulting in musculoskeletal disorders (MSDs)","author":"U.S. Bureau of Labor Statistics","year":"2020"},{"key":"bibr47-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1109\/ICRA40945.2020.9197296"},{"key":"bibr48-00187208231177574","doi-asserted-by":"publisher","DOI":"10.17973\/MMSJ.2016_06_201611"},{"key":"bibr49-00187208231177574","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2020.07.061"},{"key":"bibr50-00187208231177574","unstructured":"Yazdani A., Novin R. S., Merryweather A., Hermans T. (2021). Ergonomically intelligent physical human-robot interaction: Postural estimation, assessment, and optimization.\n                      ArXiv Preprint ArXiv:2108.05971\n                      ."},{"key":"bibr51-00187208231177574","doi-asserted-by":"publisher","DOI":"10.4103\/ijoem.IJOEM_23_18"}],"container-title":["Human Factors: The Journal of the Human Factors and Ergonomics Society"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00187208231177574","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/00187208231177574","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00187208231177574","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00187208231177574","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T07:53:55Z","timestamp":1777449235000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/00187208231177574"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,22]]},"references-count":49,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2024,6]]}},"alternative-id":["10.1177\/00187208231177574"],"URL":"https:\/\/doi.org\/10.1177\/00187208231177574","relation":{},"ISSN":["0018-7208","1547-8181"],"issn-type":[{"value":"0018-7208","type":"print"},{"value":"1547-8181","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,22]]}}}