Myoelectric Hand Gesture Recognition using Variational Autoencoder and Sensor Fusion

Myoelectric Hand Gesture Recognition using Variational Autoencoder and Sensor Fusion | 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 Article Myoelectric Hand Gesture Recognition using Variational Autoencoder and Sensor Fusion Keith Currier, JIRUI FU, Nasrin Bayat, Yanjie Fu, Jong-Hwan Kim, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4461748/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 Pattern recognition-based myoelectric control schemes aim to classify surface electromyographic (sEMG) signals generated by skeletal muscles for use in prosthetics or wearable robots. Many factors impact the EMG signal quality and reliability, such as motion artifacts, electrode shift, and reduced conductivity over time, which calls for robust pattern recognition-based myoelectric control schemes. The manifold hypothesis – a breakdown of high-dimensional space to a lower-dimensional representation to explain how the higher-dimensional space operates – provides a framework to discover the representation or manifold of multimodal biological signals. This paper presents a pattern recognition-based myoelectric control scheme that creates compressed latent space representations using a contrastive variational autoencoder (VAE) with an integrated classifier. The VAE model was designed, trained with a secondary dataset of hand gestures, and validated subjectwise as well as for the group. The individual subject data yielded distinct and separable latent spaces with high classification accuracy ranging from 75.1% to 91.7%. Further, the model was optimized to improve classification accuracy, reaching 87.45% to 97.49%. The model architecture was generalizable across the subjects, and the compressed latent space achieved high performance when the representation was separable and distinct. The proposed VAE model and latent space representation demonstrate its feasibility and utility for use in myoelectric controls. Biological sciences/Biological techniques/Bioinformatics Biological sciences/Biological techniques Full Text Additional Declarations No competing interests reported. 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-4461748","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":316503086,"identity":"aa7cda2d-5e92-428d-ba9b-0ae933a02a86","order_by":0,"name":"Keith Currier","email":"","orcid":"","institution":"University of Florida","correspondingAuthor":false,"prefix":"","firstName":"Keith","middleName":"","lastName":"Currier","suffix":""},{"id":316503085,"identity":"136edbe1-5ad2-4534-ad96-263ee061f672","order_by":1,"name":"JIRUI FU","email":"","orcid":"","institution":"University of Central Florida","correspondingAuthor":false,"prefix":"","firstName":"JIRUI","middleName":"","lastName":"FU","suffix":""},{"id":316503087,"identity":"af3e08ca-2315-4ae7-a912-162defb16026","order_by":2,"name":"Nasrin Bayat","email":"","orcid":"","institution":"University of Central Florida","correspondingAuthor":false,"prefix":"","firstName":"Nasrin","middleName":"","lastName":"Bayat","suffix":""},{"id":316503088,"identity":"df5752c6-ddc4-4c9c-9eb0-712f96017659","order_by":3,"name":"Yanjie Fu","email":"","orcid":"","institution":"Arizona State University","correspondingAuthor":false,"prefix":"","firstName":"Yanjie","middleName":"","lastName":"Fu","suffix":""},{"id":319864634,"identity":"05dc5cfc-61c2-4af5-90c7-9e9e47804810","order_by":4,"name":"Jong-Hwan Kim","email":"","orcid":"","institution":"AI R\u0026D center at Korea Military Academy","correspondingAuthor":false,"prefix":"","firstName":"Jong-Hwan","middleName":"","lastName":"Kim","suffix":""},{"id":316503089,"identity":"b755e0cd-7d4d-43f1-87f7-c81ced03c2e0","order_by":5,"name":"Joon-Hyuk Park","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvElEQVRIiWNgGAWjYBACxgYIbcAPJD4wMDCToEWygYFxBlFaYMDA4ACxWpjbm489Lqi5Z2x8I/lhA0OFdWIDQYf1HEs3nnGs2MzsRpphA8OZdCK0zMgxk+ZhS7Axu5HD/oCx7TCxWv4l2BjPyGFsYPxHrBbetgQzAwmQlgZitPQcS5Oe2ZdgLHHmmWFDAtBjBLUYAkNMuuBbgmF/OzDEPtRYyxLW0oAcFwmElIOAPANJMT4KRsEoGAUjEgAArto9CRe1uDsAAAAASUVORK5CYII=","orcid":"","institution":"University of Central Florida","correspondingAuthor":true,"prefix":"","firstName":"Joon-Hyuk","middleName":"","lastName":"Park","suffix":""}],"badges":[],"createdAt":"2024-05-22 14:51:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4461748/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4461748/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73730706,"identity":"fb7b50f0-ea86-408b-b8eb-21e555d2e155","added_by":"auto","created_at":"2025-01-14 05:33:42","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2766572,"visible":true,"origin":"","legend":"","description":"","filename":"KeithVAEResubmit.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4461748/v1_covered_d56afbec-3381-4b58-bee6-06589cfc5cfb.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Myoelectric Hand Gesture Recognition using Variational Autoencoder and Sensor Fusion","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-4461748/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4461748/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Pattern recognition-based myoelectric control schemes aim to classify surface electromyographic (sEMG) signals generated by skeletal muscles for use in prosthetics or wearable robots. Many factors impact the EMG signal quality and reliability, such as motion artifacts, electrode shift, and reduced conductivity over time, which calls for robust pattern recognition-based myoelectric control schemes. The manifold hypothesis – a breakdown of high-dimensional space to a lower-dimensional representation to explain how the higher-dimensional space operates – provides a framework to discover the representation or manifold of multimodal biological signals. This paper presents a pattern recognition-based myoelectric control scheme that creates compressed latent space representations using a contrastive variational autoencoder (VAE) with an integrated classifier. The VAE model was designed, trained with a secondary dataset of hand gestures, and validated subjectwise as well as for the group. The individual subject data yielded distinct and separable latent spaces with high classification accuracy ranging from 75.1\\% to 91.7\\%. Further, the model was optimized to improve classification accuracy, reaching 87.45\\% to 97.49\\%. The model architecture was generalizable across the subjects, and the compressed latent space achieved high performance when the representation was separable and distinct. The proposed VAE model and latent space representation demonstrate its feasibility and utility for use in myoelectric controls. ","manuscriptTitle":"Myoelectric Hand Gesture Recognition using Variational Autoencoder and Sensor Fusion","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-06-21 06:28:22","doi":"10.21203/rs.3.rs-4461748/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":"17f23833-75ff-4ed0-8d36-0265664e4291","owner":[],"postedDate":"June 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33469269,"name":"Biological sciences/Biological techniques/Bioinformatics"},{"id":33469270,"name":"Biological sciences/Biological techniques"}],"tags":[],"updatedAt":"2025-01-14T05:24:48+00:00","versionOfRecord":[],"versionCreatedAt":"2024-06-21 06:28:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4461748","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4461748","identity":"rs-4461748","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}