{"id":1737,"date":"2024-10-11T01:37:42","date_gmt":"2024-10-11T01:37:42","guid":{"rendered":"https:\/\/sbia.org.br\/lnlm\/?page_id=1737"},"modified":"2024-10-11T01:37:42","modified_gmt":"2024-10-11T01:37:42","slug":"vol22-no2-art4","status":"publish","type":"page","link":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/","title":{"rendered":"Comparison of SEMG-Based Hand Gesture Classifiers"},"content":{"rendered":"<p>Guilherme Caldeira De Lello <a href=\"https:\/\/orcid.org\/0000-0001-6529-7034\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Gabriel da Silva Chaves <a href=\"https:\/\/orcid.org\/0000-0002-7547-7597\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>, Juliano Freitas Caldeira <a href=\"https:\/\/orcid.org\/0009-0002-2321-0222\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a>&#038; Markus V. S. Lima <a href=\"https:\/\/orcid.org\/0000-0002-7333-025X\"><img loading=\"lazy\" decoding=\"async\" class=\"alignnone size-full wp-image-1167\" src=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" alt=\"orcid\" width=\"20\" height=\"20\" \/><\/a><\/p>\n<p><strong>Abstract:<\/strong> Machine learning techniques have shown success in classifying hand gestures. As the prevalence of prosthetic devices continues to rise, the adoption of non-invasive technologies, such as surface electromyography (sEMG), becomes paramount. This study systematically assesses the isolated influence of classification algorithms within hand gesture recognition (HGR) systems using sEMG data and dynamic time warping (DTW) based features. This approach effectively handles temporal variations in sEMG signals by leveraging DTW, ensuring input features are invariant to gesture speed. Six supervised learning classifiers were evaluated: the multilayer perceptron, support vector machine, logistic regression, linear discriminant analysis, k-nearest neighbors, and decision tree. Cross-validation was employed to fine-tune the segmentation hyperparameters, significantly improving results. To ensure reproducibility, the source code has been made available, the proposed system design has been detailed, and the evaluation protocols have been described. Our findings indicate that logistic regression outperformed other classifiers in this setup, achieving 95.2% accuracy in classifying six hand movements from ten healthy individuals, representing a 1.6% improvement over the best previously reported performance using the same publicly available dataset. Future research will assess the proposed HGR system\u2019s generalization capability on larger datasets suitable for training more complex classifiers, including deep learning models.<\/p>\n<p><strong>Keywords:<\/strong> Hand gesture classifier, segmentation, sEMG, dynamic time warping, artificial neural networks, support vector machines, logistic regression, linear discriminant analysis, k-nearest neighbors, decision tree.<\/p>\n<p><strong>DOI code:<\/strong> <a href=\"http:\/\/dx.doi.org\/10.21528\/lnlm-vol22-no2-art4\">10.21528\/lnlm-vol22-no2-art4<\/a><\/p>\n<p><strong>PDF file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2024\/12\/vol22-no2-art4.pdf\">vol22-no2-art4.pdf<\/a><\/p>\n<p><strong>BibTex file:<\/strong> <a href=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2024\/12\/vol22-no2-art4.bib\">vol22-no2-art4.bib<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>Guilherme Caldeira De Lello , Gabriel da Silva Chaves , Juliano Freitas Caldeira &#038; Markus V. S. Lima Abstract: Machine learning techniques have shown success in classifying hand gestures. As the prevalence of prosthetic devices continues to rise, the adoption <a href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/\" class=\"read-more\">Read More &#8230;<\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":1710,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-1737","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v26.9 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Comparison of SEMG-Based Hand Gesture Classifiers - Learning and NonLinear Models<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/\" \/>\n<meta property=\"og:locale\" content=\"pt_BR\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Comparison of SEMG-Based Hand Gesture Classifiers - Learning and NonLinear Models\" \/>\n<meta property=\"og:description\" content=\"Guilherme Caldeira De Lello , Gabriel da Silva Chaves , Juliano Freitas Caldeira &#038; Markus V. S. Lima Abstract: Machine learning techniques have shown success in classifying hand gestures. As the prevalence of prosthetic devices continues to rise, the adoption Read More ...\" \/>\n<meta property=\"og:url\" content=\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/\" \/>\n<meta property=\"og:site_name\" content=\"Learning and NonLinear Models\" \/>\n<meta property=\"og:image\" content=\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/\",\"name\":\"Comparison of SEMG-Based Hand Gesture Classifiers - Learning and NonLinear Models\",\"isPartOf\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\"},\"primaryImageOfPage\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/#primaryimage\"},\"image\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/#primaryimage\"},\"thumbnailUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\",\"datePublished\":\"2024-10-11T01:37:42+00:00\",\"breadcrumb\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/#breadcrumb\"},\"inLanguage\":\"pt-BR\",\"potentialAction\":[{\"@type\":\"ReadAction\",\"target\":[\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/\"]}]},{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/#primaryimage\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\",\"contentUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg\"},{\"@type\":\"BreadcrumbList\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/#breadcrumb\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Browse issues\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Learning &#038; Nonlinear Models &#8211; L&#038;NLM &#8211; Volume 22 &#8211; N\u00famero 2\",\"item\":\"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Comparison of SEMG-Based Hand Gesture Classifiers\"}]},{\"@type\":\"WebSite\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#website\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/\",\"name\":\"Learning and NonLinear Models\",\"description\":\"\",\"publisher\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#organization\"},\"potentialAction\":[{\"@type\":\"SearchAction\",\"target\":{\"@type\":\"EntryPoint\",\"urlTemplate\":\"https:\/\/sbia.org.br\/lnlm\/?s={search_term_string}\"},\"query-input\":{\"@type\":\"PropertyValueSpecification\",\"valueRequired\":true,\"valueName\":\"search_term_string\"}}],\"inLanguage\":\"pt-BR\"},{\"@type\":\"Organization\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#organization\",\"name\":\"Learning and NonLinear Models\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/\",\"logo\":{\"@type\":\"ImageObject\",\"inLanguage\":\"pt-BR\",\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/\",\"url\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png\",\"contentUrl\":\"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png\",\"width\":398,\"height\":94,\"caption\":\"Learning and NonLinear Models\"},\"image\":{\"@id\":\"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/\"}}]}<\/script>\n<!-- \/ Yoast SEO plugin. -->","yoast_head_json":{"title":"Comparison of SEMG-Based Hand Gesture Classifiers - Learning and NonLinear Models","robots":{"index":"index","follow":"follow","max-snippet":"max-snippet:-1","max-image-preview":"max-image-preview:large","max-video-preview":"max-video-preview:-1"},"canonical":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/","og_locale":"pt_BR","og_type":"article","og_title":"Comparison of SEMG-Based Hand Gesture Classifiers - Learning and NonLinear Models","og_description":"Guilherme Caldeira De Lello , Gabriel da Silva Chaves , Juliano Freitas Caldeira &#038; Markus V. 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As the prevalence of prosthetic devices continues to rise, the adoption Read More ...","og_url":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/","og_site_name":"Learning and NonLinear Models","og_image":[{"url":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg","type":"","width":"","height":""}],"twitter_card":"summary_large_image","schema":{"@context":"https:\/\/schema.org","@graph":[{"@type":"WebPage","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/","url":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/","name":"Comparison of SEMG-Based Hand Gesture Classifiers - Learning and NonLinear Models","isPartOf":{"@id":"https:\/\/sbia.org.br\/lnlm\/#website"},"primaryImageOfPage":{"@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/#primaryimage"},"image":{"@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/#primaryimage"},"thumbnailUrl":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg","datePublished":"2024-10-11T01:37:42+00:00","breadcrumb":{"@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/#breadcrumb"},"inLanguage":"pt-BR","potentialAction":[{"@type":"ReadAction","target":["https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/"]}]},{"@type":"ImageObject","inLanguage":"pt-BR","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/#primaryimage","url":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg","contentUrl":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/sites\/4\/2020\/09\/orcid.jpg"},{"@type":"BreadcrumbList","@id":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/vol22-no2-art4\/#breadcrumb","itemListElement":[{"@type":"ListItem","position":1,"name":"Browse issues","item":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/"},{"@type":"ListItem","position":2,"name":"Learning &#038; Nonlinear Models &#8211; L&#038;NLM &#8211; Volume 22 &#8211; N\u00famero 2","item":"https:\/\/sbia.org.br\/lnlm\/publicacoes\/vol22-no2\/"},{"@type":"ListItem","position":3,"name":"Comparison of SEMG-Based Hand Gesture Classifiers"}]},{"@type":"WebSite","@id":"https:\/\/sbia.org.br\/lnlm\/#website","url":"https:\/\/sbia.org.br\/lnlm\/","name":"Learning and NonLinear Models","description":"","publisher":{"@id":"https:\/\/sbia.org.br\/lnlm\/#organization"},"potentialAction":[{"@type":"SearchAction","target":{"@type":"EntryPoint","urlTemplate":"https:\/\/sbia.org.br\/lnlm\/?s={search_term_string}"},"query-input":{"@type":"PropertyValueSpecification","valueRequired":true,"valueName":"search_term_string"}}],"inLanguage":"pt-BR"},{"@type":"Organization","@id":"https:\/\/sbia.org.br\/lnlm\/#organization","name":"Learning and NonLinear Models","url":"https:\/\/sbia.org.br\/lnlm\/","logo":{"@type":"ImageObject","inLanguage":"pt-BR","@id":"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/","url":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png","contentUrl":"https:\/\/sbia.org.br\/lnlm\/wp-content\/uploads\/2021\/07\/logo-lnlm.png","width":398,"height":94,"caption":"Learning and NonLinear Models"},"image":{"@id":"https:\/\/sbia.org.br\/lnlm\/#\/schema\/logo\/image\/"}}]}},"_links":{"self":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/1737","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/comments?post=1737"}],"version-history":[{"count":1,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/1737\/revisions"}],"predecessor-version":[{"id":1738,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/1737\/revisions\/1738"}],"up":[{"embeddable":true,"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/pages\/1710"}],"wp:attachment":[{"href":"https:\/\/sbia.org.br\/lnlm\/wp-json\/wp\/v2\/media?parent=1737"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}