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The initial study on a machine-learning-based model is proposed to identify the 4-state motion of rehabilitation exercise using wearable sensors on the lower limbs. The study analyses the impact of the feature extracted from the sensor signals while classifying using the linear kernel of the support vector machine method. The evaluation results show that the method has an average accuracy of 95.83% using the raw sensor signal, which has more impact than the sensor fused Euler and joint angles in the state prediction model. This study will both enable real-time biofeedback and provide complementary support to clinical assessment and performance tracking.<\/p>","DOI":"10.4018\/ijitn.2020010102","type":"journal-article","created":{"date-parts":[[2019,10,15]],"date-time":"2019-10-15T14:35:32Z","timestamp":1571150132000},"page":"15-27","source":"Crossref","is-referenced-by-count":1,"title":["Lower-Limb Rehabilitation at Home"],"prefix":"10.4018","volume":"12","author":[{"given":"Seanglidet","family":"Yean","sequence":"first","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bu Sung","family":"Lee","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7618-1472","authenticated-orcid":true,"given":"Chai Kiat","family":"Yeo","sequence":"additional","affiliation":[{"name":"Nanyang Technological University, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"IJITN.2020010102-0","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-008-0225-y"},{"key":"IJITN.2020010102-1","doi-asserted-by":"publisher","DOI":"10.1109\/SCAN.2006.25"},{"key":"IJITN.2020010102-2","doi-asserted-by":"publisher","DOI":"10.1109\/IEMBS.2011.6090940"},{"key":"IJITN.2020010102-3","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijge.2013.08.014"},{"key":"IJITN.2020010102-4","doi-asserted-by":"publisher","DOI":"10.1016\/j.gaitpost.2013.03.029"},{"key":"IJITN.2020010102-5","doi-asserted-by":"publisher","DOI":"10.1093\/ptj\/71.6.455"},{"key":"IJITN.2020010102-6","doi-asserted-by":"publisher","DOI":"10.1016\/j.patrec.2005.10.010"},{"key":"IJITN.2020010102-7","doi-asserted-by":"publisher","DOI":"10.1186\/1743-0003-11-158"},{"key":"IJITN.2020010102-8","doi-asserted-by":"publisher","DOI":"10.1177\/0363546515617742"},{"issue":"1","key":"IJITN.2020010102-9","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/s00530-013-0332-2","article-title":"Rule-based approach to recognizing human body poses and gestures in real time.","volume":"20","author":"T.Hachaj","year":"2014","journal-title":"Multimedia Systems"},{"key":"IJITN.2020010102-10","doi-asserted-by":"publisher","DOI":"10.1007\/s00530-013-0332-2"},{"key":"IJITN.2020010102-11","unstructured":"Injury Clinic Ltd. 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