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People with finger problems may find it difficult to operate wheelchairs using the conventional joystick control method. Therefore, in this research study, a hand gesture-based control method is developed for operating an electric-powered wheelchair (EPW). This study selected a comfort-based hand position to determine the stop maneuver. An additional exploration was undertaken to investigate four gesture recognition methods: linear regression (LR), regularized linear regression (RLR), decision tree (DT), and multi-class support vector machine (MC-SVM). The first two methods, LR and RLR, have promising accuracy values of <jats:bold>94.85%<\/jats:bold> and <jats:bold>95.88%<\/jats:bold>, respectively, but each new user must be trained. To overcome this limitation, this study explored two user-independent classification methods: MC-SVM and DT. These methods effectively addressed the finger dependency issue and demonstrated remarkable success in recognizing gestures across different users. MC-SVM has about <jats:bold>99.05%<\/jats:bold> of both precision and accuracy, and the DT has about <jats:bold>97.77%<\/jats:bold> accuracy and precision. All six participants were successful in controlling the EPW without any collisions. According to the experimental results, the proposed approach has high accuracy and can address finger dependency issues.<\/jats:p>","DOI":"10.1007\/s11517-023-02921-z","type":"journal-article","created":{"date-parts":[[2023,9,26]],"date-time":"2023-09-26T02:01:37Z","timestamp":1695693697000},"page":"167-182","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Electric powered wheelchair control using user-independent classification methods based on surface electromyography signals"],"prefix":"10.1007","volume":"62","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1438-9403","authenticated-orcid":false,"given":"Hassam","family":"Iqbal","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinchuan","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rifai","family":"Chai","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sivachandran","family":"Chandrasekaran","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,26]]},"reference":[{"key":"2921_CR1","unstructured":"World Health Organization and International Spinal Cord Society (2013) International perspectives on spinal cord injury, World Health Organization"},{"key":"2921_CR2","first-page":"10","volume-title":"Electric wheelchair control using wrist rotation based on analysis of muscle fatigue","author":"MI Rusydi","year":"2022","unstructured":"Rusydi MI, Hadi K, Setiawan AW, Reni I, Nugroho H, Windasari N (2022) Electric wheelchair control using wrist rotation based on analysis of muscle fatigue. 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