Exploring Machine Learning Algorithms for Optimized Muscle Activity Detection Using sEMG Signals: A Systematic Review

Exploring Machine Learning Algorithms for Optimized Muscle Activity Detection Using sEMG Signals: A Systematic Review | 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 Systematic Review Exploring Machine Learning Algorithms for Optimized Muscle Activity Detection Using sEMG Signals: A Systematic Review Khushboo Danish, Dr Saad Jawaid Ahmed Khan, Dr Ali Asghar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9048584/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract sEMG signals are considered important in development of prosthetics, clinical diagnosis and rehabilitation sciences. The most important feature in signal acquisition is muscular-activity timing that determines muscle onset/offset timing representing indicating muscle activity. Numerous research propositions are devised, although the standard method is not completed yet. The aim of this systematic study is to review the reliability of multiple machine-learning-based approach for detection of muscle activity through sEMG signals to compare the performance with earlier study. IEEE Xplore, Google Scholar and Science Direct websites were preferred to retrieve resources for foundation of our research publications between 2010 and 2023 by implementation of suitable keywords as ‘‘muscle activity and Machine Learning .’’ With proper protocol of careful screening, 23 publications were chosen in our selected criteria in this written systematic review. The work scrutiny reveals that provided Machine Learning (ML) techniques comprised of Artificial Neural Network (ANN), Support Vector Machine (SVM), K-nearest Neighbor, and TKEO. Artificial Neural Networks were reflected to demonstrate the major accuracy of 95% proceeding by SVM providing 92% and TKEO generating 85%. These techniques are majorly used for proposing protocol examinations involved in EMG parameters. Values of parameters such as accuracy, (SNR), threshold, error were uprooted from the studies and relevant conclusion were also made through consideration of comparisons provided in data statistics. Eventually, with this provided systematic review, a brief compilation of the studies was executed that comprises of how Machine Learning (ML) methods have been implemented for the efficient detection of muscle activity. Biomedical Engineering Machine Learning EMG Signals Muscle activity Classifiers Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-9048584","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":601761501,"identity":"3266925a-4193-4846-b63f-07365f902c4f","order_by":0,"name":"Khushboo Danish","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYBACCQkGBmbGBiDrwOEDBz4AaTZ2orUcPJZ4cAZICzPRWg6fUT7MAxIipEVydo/x58IdNnJ8x84wHLb5tU2ej5mB8cPHHNxapGXOmEnPPJNmLHnm7IHDuX23DduYGZglZ27DrUVOIseMmbftcOKGG+cSDuf23GYEamFj5sWvxfgzWMv9NwaHLXtu2xPUIi2RYyAN1nLgjMFhhh+3EwlqkZyRVibNC/LLgWMJB3sbbie3MTM24/WLxI3kzZ95QSF24PDhDz/+3Lad39588MNHPFpQAWMbmGwgVj0I/CFF8SgYBaNgFIwUAACVHFnZnQ0eTwAAAABJRU5ErkJggg==","orcid":"","institution":"Ziauddin University","correspondingAuthor":true,"prefix":"","firstName":"Khushboo","middleName":"","lastName":"Danish","suffix":""},{"id":601761584,"identity":"68910260-b291-49cd-ac8a-12f876923639","order_by":1,"name":"Dr Saad Jawaid Ahmed Khan","email":"","orcid":"","institution":"Ziauddin University","correspondingAuthor":false,"prefix":"Dr","firstName":"Saad","middleName":"Jawaid Ahmed","lastName":"Khan","suffix":""},{"id":601761585,"identity":"80459384-2475-45e5-8155-606aa233c4d1","order_by":2,"name":"Dr Ali Asghar","email":"","orcid":"","institution":"Salim Habib University","correspondingAuthor":false,"prefix":"Dr","firstName":"Ali","middleName":"","lastName":"Asghar","suffix":""}],"badges":[],"createdAt":"2026-03-06 09:21:16","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9048584/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9048584/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104214637,"identity":"6e02e153-42e9-4cd6-854b-12d1788e99d8","added_by":"auto","created_at":"2026-03-09 08:42:40","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":628395,"visible":true,"origin":"","legend":"","description":"","filename":"systematicreview.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9048584/v1_covered_9a30e152-f607-42ec-b888-8f56a940818c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eExploring Machine Learning Algorithms for Optimized Muscle Activity Detection Using sEMG Signals: A Systematic Review\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"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":"Machine Learning, EMG Signals, Muscle activity, Classifiers ","lastPublishedDoi":"10.21203/rs.3.rs-9048584/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9048584/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003esEMG signals are considered \u0026nbsp;important in development of prosthetics, clinical diagnosis and rehabilitation sciences. The most important feature in signal acquisition is muscular-activity timing that determines muscle onset/offset timing representing indicating muscle activity. Numerous research propositions are devised, although the standard method is not completed yet. The aim of this systematic study is to review the reliability of multiple machine-learning-based approach for detection of muscle activity through sEMG signals to compare the performance with earlier study. IEEE Xplore, Google Scholar and Science Direct websites were preferred to retrieve resources for foundation of our research publications between 2010 and 2023 by implementation of suitable keywords as ‘‘muscle activity and Machine Learning .’’ With proper protocol of careful screening, 23 publications were chosen in our selected criteria in this written systematic review. 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