{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:32:43Z","timestamp":1775230363822,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T00:00:00Z","timestamp":1582156800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Beijing Municipal Science and Technology Commission","award":["Z181100003118001"],"award-info":[{"award-number":["Z181100003118001"]}]},{"DOI":"10.13039\/501100018537","name":"National Science and Technology Major Project","doi-asserted-by":"publisher","award":["2018ZX04032002"],"award-info":[{"award-number":["2018ZX04032002"]}],"id":[{"id":"10.13039\/501100018537","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Fundamental Research Funds for the Central Universities","award":["3142018047"],"award-info":[{"award-number":["3142018047"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>During human\u2013robot collaborations (HRC), robot systems must accurately perceive the actions and intentions of humans. The present study proposes the classification of standing postures from standing-pressure images, by which a robot system can predict the intended actions of human workers in an HRC environment. To this end, it explores deep learning based on standing-posture recognition and a multi-recognition algorithm fusion method for HRC. To acquire the pressure-distribution data, ten experimental participants stood on a pressure-sensing floor embedded with thin-film pressure sensors. The pressure data of nine standing postures were obtained from each participant. The human standing postures were discriminated by seven classification algorithms. The results of the best three algorithms were fused using the Dempster\u2013Shafer evidence theory to improve the accuracy and robustness. In a cross-validation test, the best method achieved an average accuracy of 99.96%. The convolutional neural network classifier and data-fusion algorithm can feasibly classify the standing postures of human workers.<\/jats:p>","DOI":"10.3390\/s20041158","type":"journal-article","created":{"date-parts":[[2020,2,21]],"date-time":"2020-02-21T10:49:16Z","timestamp":1582282156000},"page":"1158","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Standing-Posture Recognition in Human\u2013Robot Collaboration Based on Deep Learning and the Dempster\u2013Shafer Evidence Theory"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4231-6196","authenticated-orcid":false,"given":"Guan","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing 100022, China"},{"name":"Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100022, China"},{"name":"North China Institute of Science and Technology, Langfang 065201, China"}]},{"given":"Zhifeng","family":"Liu","sequence":"additional","affiliation":[{"name":"Institute of Advanced Manufacturing and Intelligent Technology, Beijing University of Technology, Beijing 100022, China"},{"name":"Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100022, China"}]},{"given":"Ligang","family":"Cai","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100022, China"},{"name":"Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing, Beijing 100022, China"}]},{"given":"Jun","family":"Yan","sequence":"additional","affiliation":[{"name":"Beijing Key Laboratory of Advanced Manufacturing Technology, Beijing University of Technology, Beijing 100022, China"},{"name":"Mechanical Industry Key Laboratory of Heavy Machine Tool Digital Design and Testing, Beijing 100022, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"856","DOI":"10.1109\/LRA.2017.2655565","article-title":"Adaptive Task Scheduling for an Assembly Task Co-worker Robot Based on Incremental Learning of a Human\u2019s Motion Pattern","volume":"2","author":"Kinugawa","year":"2017","journal-title":"IEEE Robot. 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