Design and Validation of a Three-Channel EMG Classification System Using Indigenously Developed Dry Electrodes

Design and Validation of a Three-Channel EMG Classification System Using Indigenously Developed Dry Electrodes | 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 Article Design and Validation of a Three-Channel EMG Classification System Using Indigenously Developed Dry Electrodes Zubaid Ali Zafar, Ubaid us Sami, Arhama Riaz, Sadia Shakil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8927524/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 Reliable and affordable electromyography (EMG) signal acquisition is a critical requirement for myoelectric prosthetic control systems. Conventional gel-based electrodes offer good signal quality but suffer from limitations related to single- use design and long-term stability while commercial EMG acquisition systems are very costly. This paper presents a comparative study of custom-designed copper-based dry electrodes and commercially available gel electrodes for sur- face EMG acquisition, along with the indigenous development of a low-cost and customizable data acquisition module as an alternative to commercial solutions. The performance of both electrode types is evaluated through EMG signals acquired from forearm muscles. Computationally efficient time-domain features are extracted and used to train an artificial neural network (ANN) for move- ment classification. The trained ANN model is deployed on a microcontroller and integrated into a custom-built active prosthetic hand, enabling real-time control without reliance on external computing hardware. Experimental results demonstrate that the proposed dry electrodes provide stable and reliable EMG signals, while the developed acquisition system supports accurate classification and embedded implementation. The presented approach validates the feasibil- ity of an indigenously developed, fully embedded, cost-effective EMG-based prosthetic control system suitable for practical and wearable applications. Biomedical Engineering EMG ANN sEMG MEAP KNN RF SVM ADC 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. 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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-8927524","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":594558549,"identity":"8e310264-b4db-4913-9825-9dcbbb1d9689","order_by":0,"name":"Zubaid Ali Zafar","email":"","orcid":"","institution":"Insititue of Space Technology Islamabad Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Zubaid","middleName":"Ali","lastName":"Zafar","suffix":""},{"id":594558550,"identity":"b2ae26a0-4206-4795-93c6-78c616c10dcc","order_by":1,"name":"Ubaid us Sami","email":"","orcid":"","institution":"Insititue of Space Technology Islamabad Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Ubaid","middleName":"us","lastName":"Sami","suffix":""},{"id":594558551,"identity":"18b95772-1fab-4456-a685-24a6f3123b86","order_by":2,"name":"Arhama Riaz","email":"","orcid":"","institution":"National University of Science and Technology Islamabad Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Arhama","middleName":"","lastName":"Riaz","suffix":""},{"id":594558552,"identity":"4799db50-192a-483c-bcdd-57c77c33bab9","order_by":3,"name":"Sadia Shakil","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYHACgwMMDAcYJJiZGx8AGQYgEQbGhgRitDA2GxCthQGshYGxTYIoLfLthzce+PHnjrxkO2Nbxc+2O8YM7M3bJBh3pOG24kxawcHetmeGs5kZ224CGWYMPMfKJBjP5OBxVY7BAd6Gw4zzgFpuM7YdtmGQyDGTANqI22H9bwwO/vlz2B6kpRisRf4Nfi0MN3IMDvOwHU4EOQyIDpsxSPCAtOBx2I1nBYdl2w4nz2xmbJbsOXfYmI0nrdgisQ239+X7kzd/fPPnsO2M84cPfvhRdtiwn/3wxhsf25JxOwwDsIGIBBI0jIJRMApGwSjABABm2VxX+bAgXgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-7218-7482","institution":"The Chinese University of Hong Kong","correspondingAuthor":true,"prefix":"","firstName":"Sadia","middleName":"","lastName":"Shakil","suffix":""}],"badges":[],"createdAt":"2026-02-20 16:03:43","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8927524/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8927524/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103504741,"identity":"f27d61d6-d575-4e24-95e1-ec264b2ff11e","added_by":"auto","created_at":"2026-02-26 13:21:12","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":559583,"visible":true,"origin":"","legend":"","description":"","filename":"EMGClassificationdeviceV5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8927524/v1_covered_00f12dc5-eb1e-40ae-8cd7-b261534ae343.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eDesign and Validation of a Three-Channel EMG Classification System Using Indigenously Developed Dry Electrodes\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Institue of Space Technology Islamabad ","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":"EMG, ANN, sEMG, MEAP, KNN, RF, SVM, ADC","lastPublishedDoi":"10.21203/rs.3.rs-8927524/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8927524/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReliable and affordable electromyography (EMG) signal acquisition is a critical requirement for myoelectric prosthetic control systems. Conventional gel-based electrodes offer good signal quality but suffer from limitations related to single- use design and long-term stability while commercial EMG acquisition systems are very costly. This paper presents a comparative study of custom-designed copper-based dry electrodes and commercially available gel electrodes for sur- face EMG acquisition, along with the indigenous development of a low-cost and customizable data acquisition module as an alternative to commercial solutions. The performance of both electrode types is evaluated through EMG signals acquired from forearm muscles. Computationally efficient time-domain features are extracted and used to train an artificial neural network (ANN) for move- ment classification. The trained ANN model is deployed on a microcontroller and integrated into a custom-built active prosthetic hand, enabling real-time control without reliance on external computing hardware. 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