{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T13:48:04Z","timestamp":1774964884624,"version":"3.50.1"},"reference-count":29,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,15]],"date-time":"2020-11-15T00:00:00Z","timestamp":1605398400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Chinese NSF","award":["61703135"],"award-info":[{"award-number":["61703135"]}]},{"name":"Goverment of Hebei Province","award":["2018"],"award-info":[{"award-number":["2018"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A novel method for recognizing the phases in bicycling of lower limb amputees using support vector machine (SVM) optimized by particle swarm optimization (PSO) is proposed in this paper. The method is essential for enhanced prosthetic knee joint control for lower limb amputees in carrying out bicycling activity. Some wireless wearable accelerometers and a knee joint angle sensor are installed in the prosthesis to obtain data on the knee joint and ankle joint horizontal, vertical acceleration signal and knee joint angle. In order to overcome the problem of high noise content in the collected data, a soft-hard threshold filter was used to remove the noise caused by the vibration. The filtered information is then used to extract the multi-dimensional feature vector for the training of SVM for performing bicycling phase recognition. The SVM is optimized by PSO to enhance its classification accuracy. The recognition accuracy of the PSO-SVM classification model on testing data is 93%, which is much higher than those of BP, SVM and PSO-BP classification models.<\/jats:p>","DOI":"10.3390\/s20226533","type":"journal-article","created":{"date-parts":[[2020,11,16]],"date-time":"2020-11-16T21:48:52Z","timestamp":1605563332000},"page":"6533","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Bicycling Phase Recognition for Lower Limb Amputees Using Support Vector Machine Optimized by Particle Swarm Optimization"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3493-3904","authenticated-orcid":false,"given":"Xinxin","family":"Li","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuojun","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinzhi","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Hebei University of Technology, Tianjin 300130, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9745-664X","authenticated-orcid":false,"given":"Jie","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Engineering, Merz Court, Newcastle University, Newcastle upon Tyne NE1 7RU, UK"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"754","DOI":"10.1109\/TNSRE.2015.2455054","article-title":"The Effects of Prosthesis Inertial Properties on Prosthetic Knee Moment and Hip Energetics Required to Achieve Able-bodied Kinematics","volume":"24","author":"Narang","year":"2016","journal-title":"IEEE Trans. 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