Comparative Evaluation of the Time, Frequency, and Time‒Frequency Domain Features of EMG Signals for Neural Network-Based Classification of Upper Limb Kinematics

Comparative Evaluation of the Time, Frequency, and Time‒Frequency Domain Features of EMG Signals for Neural Network-Based Classification of Upper Limb Kinematics | 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 Comparative Evaluation of the Time, Frequency, and Time‒Frequency Domain Features of EMG Signals for Neural Network-Based Classification of Upper Limb Kinematics Khushboo Danish, Ali Asghar, Amenah Abul Mujeeb, Dr. Imran Khan Niazi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9382414/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 Prosthetic devices have significantly evolved from passive mechanical limbs to intelligent systems capable of mimicking natural movement. Modern prosthetics aim to restore lost functionality by integrating biosignals such as electromyography (EMG), which captures electrical activity generated by muscle contractions. The EMG serves as a vital interface between human intention and prosthetic action, enabling real-time control through signal interpretation. The effectiveness of EMG-based control systems depends largely on accurate feature extraction and robust machine learning classifiers. This research presents a comparative analysis of EMG signal features across three domains—time, frequency, and time-frequency—to determine the optimal approaches for prosthetic control. EMG data, acquired via both surface (sEMG) and intramuscular (iEMG) techniques, were collected from eight healthy male participants performing five distinct hand postures and four arm positions. Neural network classifiers, particularly narrow neural networks, were applied to assess classification accuracy under two conditions: fixed arm positions (FAPs) and fixed hand postures (FHPs). The results showed that time‒frequency domain features consistently outperformed those from the time and frequency domains. In the FAP scenario, the narrow neural network achieves a maximum accuracy of 99.09% at hand rest. In the FHP scenario, the same model reached 97.9% accuracy at a 135° arm angle. The observed performance hierarchy was time-frequency > frequency > time for the FAP and time-frequency > time > frequency for the FHP. These findings emphasize the potential of time-frequency domain features and neural network classifiers in enhancing the accuracy and efficiency of EMG-based prosthetic systems, contributing to more accessible and responsive assistive technologies. Biomedical Engineering Artificial Intelligence and Machine Learning Electromyography signals Prosthetics Arm positions Hand postures Time domain Frequency domain Time‒frequency domain 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-9382414","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621150262,"identity":"f05d4eff-06f4-4d7b-bfcc-eccbfd05d05a","order_by":0,"name":"Khushboo 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Modern prosthetics aim to restore lost functionality by integrating biosignals such as electromyography (EMG), which captures electrical activity generated by muscle contractions. The EMG serves as a vital interface between human intention and prosthetic action, enabling real-time control through signal interpretation. The effectiveness of EMG-based control systems depends largely on accurate feature extraction and robust machine learning classifiers.\u003c/p\u003e \u003cp\u003eThis research presents a comparative analysis of EMG signal features across three domains\u0026mdash;time, frequency, and time-frequency\u0026mdash;to determine the optimal approaches for prosthetic control. EMG data, acquired via both surface (sEMG) and intramuscular (iEMG) techniques, were collected from eight healthy male participants performing five distinct hand postures and four arm positions. Neural network classifiers, particularly narrow neural networks, were applied to assess classification accuracy under two conditions: fixed arm positions (FAPs) and fixed hand postures (FHPs).\u003c/p\u003e \u003cp\u003eThe results showed that time‒frequency domain features consistently outperformed those from the time and frequency domains. In the FAP scenario, the narrow neural network achieves a maximum accuracy of 99.09% at hand rest. In the FHP scenario, the same model reached 97.9% accuracy at a 135\u0026deg; arm angle. The observed performance hierarchy was time-frequency\u0026thinsp;\u0026gt;\u0026thinsp;frequency\u0026thinsp;\u0026gt;\u0026thinsp;time for the FAP and time-frequency\u0026thinsp;\u0026gt;\u0026thinsp;time\u0026thinsp;\u0026gt;\u0026thinsp;frequency for the FHP.\u003c/p\u003e \u003cp\u003eThese findings emphasize the potential of time-frequency domain features and neural network classifiers in enhancing the accuracy and efficiency of EMG-based prosthetic systems, contributing to more accessible and responsive assistive technologies.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Comparative Evaluation of the Time, Frequency, and Time‒Frequency Domain Features of EMG Signals for Neural Network-Based Classification of Upper Limb Kinematics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-15 19:25:26","doi":"10.21203/rs.3.rs-9382414/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","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}}],"origin":"","ownerIdentity":"b6c82c06-041b-4c23-b865-be19d9ff472b","owner":[],"postedDate":"April 15th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":66110368,"name":"Biomedical Engineering"},{"id":66110369,"name":"Artificial Intelligence and Machine Learning"}],"tags":[],"updatedAt":"2026-04-15T19:25:27+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-15 19:25:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9382414","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9382414","identity":"rs-9382414","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}