MSDS-FusionNet: A Multi-Scale Dual-Stream Fusion Network for High-Accuracy sEMG-Based Gesture Classification

MSDS-FusionNet: A Multi-Scale Dual-Stream Fusion Network for High-Accuracy sEMG-Based Gesture Classification | 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 MSDS-FusionNet: A Multi-Scale Dual-Stream Fusion Network for High-Accuracy sEMG-Based Gesture Classification Dongyi He, Wei Liu, He Yan, Yun Zhao, Bin Jiang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6259134/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract Recently, deep-learning-based approaches have been widely applied in sEMG-based gesture recognition, however, existing methods typically focus on either time-domain or frequency-domain features, failing to leverage the complementary information from both domains, which limits the accuracy and robustness of gesture recognition systems. To address these limitations, we propose the Multi-Scale Dual-Stream Fusion Network (MSDS-FusionNet), a novel deep learning framework that integrates temporal and frequency-domain features for enhanced gesture classification accuracy. MSDS-FusionNet introduces two key innovations: the Multi-Scale Mamba (MSM) modules, which extract multi-scale temporal features through parallel convolutions with varying kernel sizes and linear-time sequence modeling with selective state spaces, enabling the capture of temporal patterns at multiple scales and the modeling of both short-term and long-term dependencies; and the Bi-directional Attention Fusion Module (BAFM), which effectively combines temporal and frequency-domain features using bi-directional attention mechanisms to fuse complementary information and improve recognition accuracy dynamically. Extensive experiments on the NinaPro dataset demonstrate that MSDS-FusionNet outperforms state-of-the-art methods, achieving accuracy improvements of up to 2.41%, 2.46%, and 1.38% on the DB2, DB3, and DB4 datasets, respectively, with final accuracies of 90.15%, 72.32%, and 87.10%. This study presents a robust and flexible solution for sEMG-based gesture recognition, effectively addressing the complexities of recognizing intricate gestures and offering significant potential for applications in prosthetics, virtual reality, and assistive technologies. The code of this study is available at https://github.com/hdy6438/MSDS-FusionNet . Biological sciences/Computational biology and bioinformatics/Data processing Biological sciences/Computational biology and bioinformatics/Machine learning Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 07 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 23 Jul, 2025 Reviews received at journal 15 Jun, 2025 Reviewers agreed at journal 01 Jun, 2025 Reviews received at journal 14 Apr, 2025 Reviews received at journal 05 Apr, 2025 Reviewers agreed at journal 29 Mar, 2025 Reviewers agreed at journal 28 Mar, 2025 Reviewers invited by journal 28 Mar, 2025 Editor assigned by journal 28 Mar, 2025 Editor invited by journal 28 Mar, 2025 Submission checks completed at journal 27 Mar, 2025 First submitted to journal 19 Mar, 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. 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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-6259134","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":430890712,"identity":"168edc15-2803-4c10-85b6-c1aa6fe38261","order_by":0,"name":"Dongyi He","email":"","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Dongyi","middleName":"","lastName":"He","suffix":""},{"id":430890713,"identity":"76e392db-e6f7-4126-8feb-3e399441cfbe","order_by":1,"name":"Wei Liu","email":"","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Liu","suffix":""},{"id":430890714,"identity":"d46b654a-a805-4dfa-91b0-9c6d58fb1033","order_by":2,"name":"He Yan","email":"","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":false,"prefix":"","firstName":"He","middleName":"","lastName":"Yan","suffix":""},{"id":430890715,"identity":"8e81af2e-5c48-4b19-a367-b9280d591d30","order_by":3,"name":"Yun Zhao","email":"","orcid":"","institution":"Chongqing Polytechnic University of Electronic Technology","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Zhao","suffix":""},{"id":430890716,"identity":"d7b76c67-eaf3-4016-ab77-9eb4ea9218df","order_by":4,"name":"Bin Jiang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/0lEQVRIiWNgGAWjYBACxhnMBw58MJDgkWdvPgAVSyCkhS3x4YwKGxnDnmMgpQaEtTBI8Bgb85xJs2G4kWNAnBbm2Q1mErxth3kYG3I+fnjb9oeBnx2o9+cOPA6bcyBNQhKohZ3h7GbJuW0GDJI9bwwYe8/g80vCMQlDkC2NvduYeYFaDIAuZGZsw6clsU0iEaiF4TDPM7AWe8JakpkNDpxJ42E4xsMGsUWCkJY5xxgfNlTY8Bj2sBlLzjlnzCNx5lnBwV48Wgxn9384/MdAwl5e/vHDD2/K5OT425M3PviJT0sDMo8HjBgYDuDWwMAgj8Ljwad0FIyCUTAKRiwAAIJpUjGUTT0xAAAAAElFTkSuQmCC","orcid":"","institution":"Chongqing University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Jiang","suffix":""}],"badges":[],"createdAt":"2025-03-19 07:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6259134/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6259134/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-34909-0","type":"published","date":"2026-01-07T15:57:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":100069479,"identity":"d5e36349-a62e-44d0-ab51-d62734b4399c","added_by":"auto","created_at":"2026-01-12 16:14:34","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1492304,"visible":true,"origin":"","legend":"","description":"","filename":"scientificreports.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6259134/v1_covered_1db4d81b-edf0-4c38-9507-a741cfad51f8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"MSDS-FusionNet: A Multi-Scale Dual-Stream Fusion Network for High-Accuracy sEMG-Based Gesture Classification","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-6259134/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6259134/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Recently, deep-learning-based approaches have been widely applied in sEMG-based gesture recognition, however, existing methods typically focus on either time-domain or frequency-domain features, failing to leverage the complementary information from both domains, which limits the accuracy and robustness of gesture recognition systems. 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