{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:11:09Z","timestamp":1760364669713,"version":"build-2065373602"},"reference-count":27,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,18]],"date-time":"2020-01-18T00:00:00Z","timestamp":1579305600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program for Intelligent Robots of the Ministry of Science and Technology","award":["2017YFB1300505"],"award-info":[{"award-number":["2017YFB1300505"]}]},{"name":"Shenzhen joint key fund project of national natural fund","award":["u1813212"],"award-info":[{"award-number":["u1813212"]}]},{"name":"national natural fund project","award":["51775415"],"award-info":[{"award-number":["51775415"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Aiming at the requirement of rapid recognition of the wearer\u2019s gait stage in the process of intelligent hybrid control of an exoskeleton, this paper studies the human body mixed motion pattern recognition technology based on multi-source feature parameters. We obtain information on human lower extremity acceleration and plantar analyze the relationship between these parameters and gait cycle studying the motion state recognition method based on feature evaluation and neural network. Based on the actual requirements of exoskeleton per use, 15 common gait patterns were determined. Using this, the studies were carried out on the time domain, frequency domain, and energy feature extraction of multi-source lower extremity motion information. The distance-based feature screening method was used to extract the optimal features. Finally, based on the multi-layer BP (back propagation) neural network, a nonlinear mapping model between feature quantity and motion state was established. The experimental results showed that the recognition accuracy in single motion mode can reach up to 98.28%, while the recognition accuracy of the two groups of experiments in mixed motion mode was found to be 92.7% and 97.4%, respectively. The feasibility and effectiveness of the model were verified.<\/jats:p>","DOI":"10.3390\/s20020537","type":"journal-article","created":{"date-parts":[[2020,1,20]],"date-time":"2020-01-20T04:27:09Z","timestamp":1579494429000},"page":"537","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Human Body Mixed Motion Pattern Recognition Method Based on Multi-Source Feature Parameter Fusion"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6436-1775","authenticated-orcid":false,"given":"Jiyuan","family":"Song","sequence":"first","affiliation":[{"name":"Institute of Robotics &amp; Intelligent Systems, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Shaanxi Key Laboratory of Intelligent Robots, Xi\u2019an 710049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6346-4932","authenticated-orcid":false,"given":"Aibin","family":"Zhu","sequence":"additional","affiliation":[{"name":"Institute of Robotics &amp; Intelligent Systems, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Shaanxi Key Laboratory of Intelligent Robots, Xi\u2019an 710049, China"}]},{"given":"Yao","family":"Tu","sequence":"additional","affiliation":[{"name":"Institute of Robotics &amp; Intelligent Systems, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Shaanxi Key Laboratory of Intelligent Robots, Xi\u2019an 710049, China"}]},{"given":"Yingxu","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Robotics &amp; Intelligent Systems, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Shaanxi Key Laboratory of Intelligent Robots, Xi\u2019an 710049, China"}]},{"given":"Muhammad Affan","family":"Arif","sequence":"additional","affiliation":[{"name":"Institute of Robotics &amp; Intelligent Systems, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Shaanxi Key Laboratory of Intelligent Robots, Xi\u2019an 710049, China"}]},{"given":"Huang","family":"Shen","sequence":"additional","affiliation":[{"name":"Institute of Robotics &amp; Intelligent Systems, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi\u2019an 710049, China"}]},{"given":"Zhitao","family":"Shen","sequence":"additional","affiliation":[{"name":"Institute of Robotics &amp; Intelligent Systems, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System, Xi\u2019an 710049, China"}]},{"given":"Xiaodong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Robotics &amp; Intelligent Systems, Xi\u2019an Jiaotong University, Xi\u2019an 710049, China"},{"name":"Shaanxi Key Laboratory of Intelligent Robots, Xi\u2019an 710049, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5595-7155","authenticated-orcid":false,"given":"Guangzhong","family":"Cao","sequence":"additional","affiliation":[{"name":"Shenzhen Key Laboratory of Electromagnetic Control, Shenzhen University, Shenzhen 518060, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1108\/02602281311294342","article-title":"Quantitative assessment of dual gait analysis based on inertial sensors with body sensor network","volume":"33","author":"Wang","year":"2013","journal-title":"Sens. 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