Mamba-SportsNet: High-Frequency Athletic Running Performance Analysis via Gated Cross-Fusion State Space Models

Mamba-SportsNet: High-Frequency Athletic Running Performance Analysis via Gated Cross-Fusion State Space Models | 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 Mamba-SportsNet: High-Frequency Athletic Running Performance Analysis via Gated Cross-Fusion State Space Models Deyi Wang, Min Ma This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8745766/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 Wearable sensor networks have revolutionized athletic training by enabling in-field monitoring, yet accurate performance assessment is hindered by the difficulty of modeling long-range dependencies in high-frequency, heterogeneous data streams. Traditional Recurrent Neural Networks struggle with memory retention over long durations, while Transformer-based architectures suffer from quadratic computational complexity, restricting their deployment on resource-constrained edge devices. To overcome these limitations, this paper proposes \textbf{Mamba-SportsNet}, a novel multimodal framework based on the Selective State Space Model (Mamba). Leveraging the linear complexity of Mamba ($O(L)$), our architecture efficiently processes synchronized kinematic (IMU) and physiological (sEMG) streams to capture both transient impact events and slowly evolving fatigue patterns. Furthermore, we introduce a \textbf{Gated Cross-Fusion (GCF)} mechanism that models the conditional dependency of mechanical execution on physiological state, allowing the network to dynamically attend to technique degradation induced by metabolic load. Extensive experiments on a dataset of 20 semi-professional athletes demonstrate that Mamba-SportsNet outperforms state-of-the-art baselines, achieving a Ground Reaction Force (GRF) estimation RMSE of \textbf{0.112 BW} and a fatigue classification accuracy of \textbf{94.2%}. Notably, the model maintains an inference latency of just \textbf{9 ms}, proving its viability for real-time, continuous monitoring in next-generation digital sports applications. Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Wearable Sensors Athletic Performance Analysis State Space Models Mamba Multimodal Fusion Deep Learning 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-8745766","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":595116470,"identity":"4fcf175d-d7d6-42f2-8d21-241162e43ed2","order_by":0,"name":"Deyi Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6klEQVRIiWNgGAWjYBACA+YDUBaYUSEhJ09QC1sClAVmnLEwNmwgSQtjW0UiwwHcqsHAnI352eeCGga7/jb2x59uzpNIYGxgfvjoBh4tlm1sxrNnHGNInnGMx0w6d5tEHjsDm7FxDj6H3W8wZuZhY0g2kO9hYwZqKWZs4GGTxqvlGPtnZp5/QC1s7I8/586RSGw4QFALjzEzbxuDnQEbg4F0bgNxWoqZZ/YxJEiA/JJzTMLYsJmQX46xb2Yu+MZgzw8Msc85NXVy8uzNDx/j0wICzAwM/xMbkLkEAUiNPRHqRsEoGAWjYKQCAFjGQ2yadZe8AAAAAElFTkSuQmCC","orcid":"","institution":"Shandong Sport University","correspondingAuthor":true,"prefix":"","firstName":"Deyi","middleName":"","lastName":"Wang","suffix":""},{"id":595116471,"identity":"685af3f3-fd9e-4a64-ab4b-cc3728691f1f","order_by":1,"name":"Min Ma","email":"","orcid":"","institution":"Shandong Sport University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Ma","suffix":""}],"badges":[],"createdAt":"2026-01-31 02:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8745766/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8745766/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107482185,"identity":"a1196357-51a4-469c-8934-7dc7228ac68b","added_by":"auto","created_at":"2026-04-22 02:22:27","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":681643,"visible":true,"origin":"","legend":"","description":"","filename":"uploadv5.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8745766/v1_covered_d6da583f-0cec-4881-af8f-86386ea57a32.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Mamba-SportsNet: High-Frequency Athletic Running Performance Analysis via Gated Cross-Fusion State Space Models","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":"Wearable Sensors, Athletic Performance Analysis, State Space Models, Mamba, Multimodal Fusion, Deep Learning","lastPublishedDoi":"10.21203/rs.3.rs-8745766/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8745766/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Wearable sensor networks have revolutionized athletic training by enabling in-field monitoring, yet accurate performance assessment is hindered by the difficulty of modeling long-range dependencies in high-frequency, heterogeneous data streams. Traditional Recurrent Neural Networks struggle with memory retention over long durations, while Transformer-based architectures suffer from quadratic computational complexity, restricting their deployment on resource-constrained edge devices. To overcome these limitations, this paper proposes \\textbf{Mamba-SportsNet}, a novel multimodal framework based on the Selective State Space Model (Mamba). Leveraging the linear complexity of Mamba ($O(L)$), our architecture efficiently processes synchronized kinematic (IMU) and physiological (sEMG) streams to capture both transient impact events and slowly evolving fatigue patterns. Furthermore, we introduce a \\textbf{Gated Cross-Fusion (GCF)} mechanism that models the conditional dependency of mechanical execution on physiological state, allowing the network to dynamically attend to technique degradation induced by metabolic load. Extensive experiments on a dataset of 20 semi-professional athletes demonstrate that Mamba-SportsNet outperforms state-of-the-art baselines, achieving a Ground Reaction Force (GRF) estimation RMSE of \\textbf{0.112 BW} and a fatigue classification accuracy of \\textbf{94.2\\%}. 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