{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,18]],"date-time":"2026-02-18T22:59:01Z","timestamp":1771455541072,"version":"3.50.1"},"reference-count":0,"publisher":"IGI Global","issue":"1","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2017,1,1]]},"abstract":"<p>Due to the serious concerns of fall risks for patients with balance disorders, it is desirable to be able to objectively identify these patients in real-time dynamic gait testing using inexpensive wearable sensors. In this work, the authors took a total of 49 gait tests from 7 human subjects (3 normal subjects and 4 patients), where each person performed 7 Dynamic Gait Index (DGI) tests by wearing a wireless gait sensor on the T4 thoracic vertebra. The raw gait data is wirelessly transmitted to a near-by PC for real-time gait data collection. To objectively identify the patients from the gait data, the authors used 4 different types of Support Vector Machine (SVM) classifiers based on the 6 features extracted from the raw gait data: Linear SVM, Quadratic SVM, Cubic SVM, and Gaussian SVM. The Linear SVM, Quadratic SVM and Cubic SVM all achieved impressive 98% classification accuracy, with 95.2% sensitivity and 100% specificity in this work. However, the Gaussian SVM classifier only achieved 87.8% accuracy, 71.7% sensitivity, and 100% specificity. The results obtained with this small number of human subjects indicates that in the near future, the authors should be able to objectively identify balance-disorder patients from normal subjects during real-time dynamic gaits testing using intelligent SVM classifiers.<\/p>","DOI":"10.4018\/ijsi.2017010102","type":"journal-article","created":{"date-parts":[[2016,10,19]],"date-time":"2016-10-19T17:26:14Z","timestamp":1476897974000},"page":"17-29","source":"Crossref","is-referenced-by-count":14,"title":["Gaits Classification of Normal vs. Patients by Wireless Gait Sensor and Support Vector Machine (SVM) Classifier"],"prefix":"10.4018","volume":"5","author":[{"given":"Taro","family":"Nakano","sequence":"first","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA & Department of Electrical & Electronic Engineering, Tokushima University, Tokushima, Japan"}]},{"given":"B.T.","family":"Nukala","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA"}]},{"given":"J.","family":"Tsay","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA"}]},{"given":"Steven","family":"Zupancic","sequence":"additional","affiliation":[{"name":"Texas Tech University Health Sciences Center (TTUHSC), Lubbock, TX, USA"}]},{"given":"Amanda","family":"Rodriguez","sequence":"additional","affiliation":[{"name":"Texas Tech University Health Sciences Center (TTUHSC), Lubbock, TX, USA"}]},{"given":"D.Y.C.","family":"Lie","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA & Texas Tech University Health Sciences Center (TTUHSC), Lubbock, TX, USA"}]},{"given":"J.","family":"Lopez","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA"}]},{"given":"Tam Q.","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Texas Tech University, Lubbock, TX, USA & Texas Tech University Health Sciences Center (TTUHSC), Lubbock, TX, USA"}]}],"member":"2432","container-title":["International Journal of Software Innovation"],"original-title":[],"language":"ng","link":[{"URL":"https:\/\/www.igi-global.com\/viewtitle.aspx?TitleId=169915","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,7]],"date-time":"2022-05-07T00:17:40Z","timestamp":1651882660000},"score":1,"resource":{"primary":{"URL":"https:\/\/services.igi-global.com\/resolvedoi\/resolve.aspx?doi=10.4018\/IJSI.2017010102"}},"subtitle":[""],"short-title":[],"issued":{"date-parts":[[2017,1,1]]},"references-count":0,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2017,1]]}},"URL":"https:\/\/doi.org\/10.4018\/ijsi.2017010102","relation":{},"ISSN":["2166-7160","2166-7179"],"issn-type":[{"value":"2166-7160","type":"print"},{"value":"2166-7179","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,1,1]]}}}