Electromyography Signal Based Hand Gesture Classification System Using Hilbert Huang Transform and Deep Neural Networks

Electromyography Signal Based Hand Gesture Classification System Using Hilbert Huang Transform and Deep Neural Networks | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Electromyography Signal Based Hand Gesture Classification System Using Hilbert Huang Transform and Deep Neural Networks Mary Vasanthi S, Jayasree T, HAITER LENIN A This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-2694748/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 01 May, 2024 Read the published version in Heliyon → Version 1 posted You are reading this latest preprint version Abstract In this paper, classification of hand gestures for the smart control of prosthetic hands is proposed. The surface Electromyography (sEMG) signals are used for classifying the hand gestures. The important attributes of the signal are extracted by finding Hilbert Huang Transform (HHT). These features are given as input to the Deep Neural Network (DNN) classifier for further classification. The experimental results show that high classification accuracy can be achieved for the proposed method compared to the other techniques. Electromyography Decomposition Features classification Neural Network Learning Full Text Cite Share Download PDF Status: Published Journal Publication published 01 May, 2024 Read the published version in Heliyon → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-2694748","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research","associatedPublications":[],"authors":[{"id":184952650,"identity":"8046ff8b-e397-45aa-9f8b-c469ca7433f7","order_by":0,"name":"Mary Vasanthi S","email":"","orcid":"","institution":"St Xavier's Catholic College of Engineering","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mary","middleName":"Vasanthi","lastName":"S","suffix":""},{"id":184952651,"identity":"042a2c77-5cfb-484b-8cd2-6d9b016987e0","order_by":1,"name":"Jayasree T","email":"","orcid":"","institution":"Government College of Engineering Tirunelveli","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jayasree","middleName":"","lastName":"T","suffix":""},{"id":184952652,"identity":"9a86003b-6df3-4088-ab8a-92257c154cab","order_by":2,"name":"HAITER LENIN A","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7klEQVRIie3PvarCMBTA8SNCu4TOpyD6ChFBEQRf5RQhvsHF4YKC4DMovoSTc0uhXYJzRQdF6Jy7XJwuHj+miwTdHPKHhBDy4xAAl+sTi8GDA4AAf8bn+80LhK5EZHymB7GyBwFAxdsrJNil5YFG+1onLKP45wz1oKDKyVhIuFEdSboU3aWKkzlBKyyoKm1TpIY2RrNUyN1wkgqCaFWQh3bi/2L0x2Sb38iYiX+2E8FT+LEsvPhKSPIU6/dDLb6QMiZaUTJX2Fzo4xS1hQTaX6P5Tvsyz1rG9HqNIB8kZmQh/0NelckbwOVyuVzPugAbrFfEy6tJTgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-4212-9163","institution":"Wollo University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"HAITER","middleName":"LENIN","lastName":"A","suffix":""}],"badges":[],"createdAt":"2023-03-15 06:42:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2694748/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2694748/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.heliyon.2024.e32211","type":"published","date":"2024-05-01T20:30:52+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":57647965,"identity":"d645085d-f8b7-4ad4-97be-07e19761af9f","added_by":"auto","created_at":"2024-06-03 20:31:01","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":893909,"visible":true,"origin":"","legend":"","description":"","filename":"ManuscriptR1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2694748/v1_covered_9d4b3aa4-0912-4da8-97e2-5a021b7ee3b8.pdf"}],"financialInterests":"","formattedTitle":"Electromyography Signal Based Hand Gesture Classification System Using Hilbert Huang Transform and Deep Neural Networks","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Electromyography, Decomposition, Features, classification, Neural Network, Learning","lastPublishedDoi":"10.21203/rs.3.rs-2694748/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2694748/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, classification of hand gestures for the smart control of prosthetic hands is proposed. 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