Improving Gesture Recognition for Amputees Based on Fusion of sEMG and Acceleration Signals Using Broad Learning System

Improving Gesture Recognition for Amputees Based on Fusion of sEMG and Acceleration Signals Using Broad Learning System | 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 Improving Gesture Recognition for Amputees Based on Fusion of sEMG and Acceleration Signals Using Broad Learning System Lei Zhang, Sha Qi, Hui Zhao, Manfredo Atzori, Henning Müller This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6716622/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Purpose Gesture recognition based on biological signals played a crucial role in the field of human-computer interaction. Despite achieving decent results in improving gesture recognition accuracy using different algorithms, significant challenges remained in enhancing recognition accuracy for multimodal signals and special patients such as upper limb amputees. Methods This paper proposed a Broad Learning System (BLS) method to recognize hand movements. The input signals fused surface electromyographic (sEMG) and acceleration signals, and the fused data were mapped into contour maps to extract features, which were transformed into two-dimensional grayscale images to achieve efficient encoding. The paper utilized the public databases "Ninapro DB2 and DB7" to evaluate the performance of the proposed method. Results Experimental results showed that the average gesture recognition accuracy of BLS reached 97.73%, with an average testing time of 0.093s, outperforming the other two algorithms, K-Nearest Neighbor (0.497s) and Binary Tree (2.412s). Moreover, the fused signals exhibited higher recognition accuracy for amputees. Conclusion In conclusion, the accuracy and real-time of the method could satisfy the requirements for controlling the prosthetic. Hence, it provided a reliable approach for research in fields such as rehabilitative medicine and prosthetic control. Gesture recognition BLS signal fusion sEMG Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 26 Oct, 2025 Reviews received at journal 28 Aug, 2025 Reviewers agreed at journal 29 Jul, 2025 Reviewers invited by journal 29 Jul, 2025 Editor assigned by journal 20 Jun, 2025 Submission checks completed at journal 23 May, 2025 First submitted to journal 21 May, 2025 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-6716622","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":492899410,"identity":"baf2bc7f-2c44-4680-b727-c61c7b68b344","order_by":0,"name":"Lei Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5klEQVRIie3PsYoCMRCA4YSA2wS2nS30GSILi6Dgq0w4WJvT5hoL4QJCbATbfQxBsI4E9ppoveXeG9jaiIrNFZJLaZG/G8jHTAiJxd6wlDFjcA7f6XNm/5NspWXbuhFmKpQI5/L+ry5RmFBCGiwAO3aWN9MayHwoVXI0XkErLAG5/SrcqQTiJlLxGXoJA6wBwdL9z7oAqq1UwIWXdEBqQGHpbsnv5BpAOLdMIJZymzy2qAACiaYtmlEO7vQxwHqSa/7pJ2Obng+XK/TSanpozothd5M4P/l7IyH4+F3o+yeJxWKx2KtuPppHYKjL0SMAAAAASUVORK5CYII=","orcid":"","institution":"Xi'an Polytechnic University","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Zhang","suffix":""},{"id":492899411,"identity":"72c96104-98fa-4202-8f36-fde176214e0e","order_by":1,"name":"Sha Qi","email":"","orcid":"","institution":"Xi'an Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Sha","middleName":"","lastName":"Qi","suffix":""},{"id":492899412,"identity":"8e4ea4ae-e74d-4666-93ad-15f985fd006d","order_by":2,"name":"Hui Zhao","email":"","orcid":"","institution":"Xi'an Polytechnic University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Zhao","suffix":""},{"id":492899413,"identity":"46ce3322-63f2-4f71-99bd-5d8a89bada59","order_by":3,"name":"Manfredo Atzori","email":"","orcid":"","institution":"University of Applied Sciences in Western Switzerland","correspondingAuthor":false,"prefix":"","firstName":"Manfredo","middleName":"","lastName":"Atzori","suffix":""},{"id":492899414,"identity":"2314954b-0074-412e-a706-dac1fed9e104","order_by":4,"name":"Henning Müller","email":"","orcid":"","institution":"University of Applied Sciences in Western Switzerland","correspondingAuthor":false,"prefix":"","firstName":"Henning","middleName":"","lastName":"Müller","suffix":""}],"badges":[],"createdAt":"2025-05-21 12:38:27","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6716622/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6716622/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88047568,"identity":"25a1f2bf-9a15-4b0e-aba0-ca2e2d9d50ed","added_by":"auto","created_at":"2025-07-31 18:44:13","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":906662,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6716622/v1_covered_c4ce8304-b028-4a55-bb21-d9fa3e58f63d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improving Gesture Recognition for Amputees Based on Fusion of sEMG and Acceleration Signals Using Broad Learning System","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"research-on-biomedical-engineering","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"rbme","sideBox":"Learn more about [Research on Biomedical Engineering](http://link.springer.com/journal/42600)","snPcode":"42600","submissionUrl":"https://submission.nature.com/new-submission/42600/3","title":"Research on Biomedical Engineering","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Gesture recognition, BLS, signal fusion, sEMG","lastPublishedDoi":"10.21203/rs.3.rs-6716622/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6716622/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e\u003cp\u003eGesture recognition based on biological signals played a crucial role in the field of human-computer interaction. Despite achieving decent results in improving gesture recognition accuracy using different algorithms, significant challenges remained in enhancing recognition accuracy for multimodal signals and special patients such as upper limb amputees.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThis paper proposed a Broad Learning System (BLS) method to recognize hand movements. The input signals fused surface electromyographic (sEMG) and acceleration signals, and the fused data were mapped into contour maps to extract features, which were transformed into two-dimensional grayscale images to achieve efficient encoding. 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