{"paper_id":"08d9f9c9-6a73-4410-aeff-c8ef0c983207","body_text":"Real-Time Continuous Assessment of Fatigue from Surface Electromyography with Deep Learning for Training Load Regulation in Elite Cyclists | 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 Real-Time Continuous Assessment of Fatigue from Surface Electromyography with Deep Learning for Training Load Regulation in Elite Cyclists Rongxuan Zhai, Guoqiang Ma, Jun Qiu, Mingxin Gong, Wenxin Niu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7863940/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 15 You are reading this latest preprint version Abstract Objective To compare linear regression and deep learning models for quantifying exercise-induced muscle fatigue from raw surface electromyography (sEMG) signals, and to identify effective approaches for accurately assessing fatigue progression during a 30-second all-out cycling sprint in elite cyclists, so as to support precise training load regulation and individualized fatigue management. Methods Fourteen elite track cyclists performed a 30-second all-out cycling sprint. Surface electromyography signals were recorded from four key lower limb muscles: rectus femoris, biceps femoris, tibialis anterior, and lateral gastrocnemius. Exercise-induced fatigue was quantified by the continuous decline in power output throughout the sprint. A deep learning model integrating convolutional neural networks (CNN), bidirectional long short-term memory (Bi-LSTM) networks, and an attention mechanism was developed to directly predict fatigue progression from raw sEMG data in an end-to-end manner. For comparison, a linear regression model was trained using eight handcrafted time- and frequency-domain EMG features: root mean square (RMS), median frequency (MF), mean power frequency (MPF), mean frequency (MNF), mean frequency deviation (MDF), spectral entropy (SE), fractal dimension (FD), and Lempel-Ziv complexity (LZC). Results The proposed deep learning model significantly outperformed all baseline models, achieving a coefficient of determination (R²) of 0.94 ± 0.02 and a mean absolute error (MAE) of 2.13 ± 0.32. Compared to the linear regression model, the deep learning approach improved R² by over 50% and reduced MAE by more than two-thirds. Conclusion This study demonstrates that an end-to-end deep learning framework can accurately and continuously track muscle fatigue directly from raw sEMG signals during high-intensity cycling. These findings highlight the superiority of deep learning over traditional feature-based linear models and provide a promising tool for real-time, individualized fatigue monitoring in elite sports performance. Surface electromyography Deep learning Real-time monitoring Fatigue monitoring Sprint cycling Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 20 Feb, 2026 Reviews received at journal 17 Feb, 2026 Reviewers agreed at journal 06 Feb, 2026 Reviews received at journal 04 Jan, 2026 Reviewers agreed at journal 12 Dec, 2025 Reviews received at journal 04 Nov, 2025 Reviewers agreed at journal 24 Oct, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers agreed at journal 22 Oct, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviewers agreed at journal 20 Oct, 2025 Reviewers invited by journal 20 Oct, 2025 Editor assigned by journal 16 Oct, 2025 Submission checks completed at journal 16 Oct, 2025 First submitted to journal 15 Oct, 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. 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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-7863940\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":539683137,\"identity\":\"23886635-2b52-4f93-8e4d-e95744c27485\",\"order_by\":0,\"name\":\"Rongxuan Zhai\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Tongji University\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Rongxuan\",\"middleName\":\"\",\"lastName\":\"Zhai\",\"suffix\":\"\"},{\"id\":539683142,\"identity\":\"a4d6c1eb-cae2-49f6-a14a-999aa3c4274f\",\"order_by\":1,\"name\":\"Guoqiang 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Learning for Training Load Regulation in Elite Cyclists\",\"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\":\"info@researchsquare.com\",\"identity\":\"bmc-sports-science-medicine-and-rehabilitation\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"ssmr\",\"sideBox\":\"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/ssmr/default.aspx\",\"title\":\"BMC Sports Science, Medicine and Rehabilitation\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Surface electromyography, Deep learning, Real-time monitoring, Fatigue monitoring, Sprint cycling\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7863940/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7863940/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eObjective\\u003c/h2\\u003e\\u003cp\\u003eTo compare linear regression and deep learning models for quantifying exercise-induced muscle fatigue from raw surface electromyography (sEMG) signals, and to identify effective approaches for accurately assessing fatigue progression during a 30-second all-out cycling sprint in elite cyclists, so as to support precise training load regulation and individualized fatigue management.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e\\u003cp\\u003eFourteen elite track cyclists performed a 30-second all-out cycling sprint. Surface electromyography signals were recorded from four key lower limb muscles: rectus femoris, biceps femoris, tibialis anterior, and lateral gastrocnemius. Exercise-induced fatigue was quantified by the continuous decline in power output throughout the sprint. A deep learning model integrating convolutional neural networks (CNN), bidirectional long short-term memory (Bi-LSTM) networks, and an attention mechanism was developed to directly predict fatigue progression from raw sEMG data in an end-to-end manner. For comparison, a linear regression model was trained using eight handcrafted time- and frequency-domain EMG features: root mean square (RMS), median frequency (MF), mean power frequency (MPF), mean frequency (MNF), mean frequency deviation (MDF), spectral entropy (SE), fractal dimension (FD), and Lempel-Ziv complexity (LZC).\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e\\u003cp\\u003eThe proposed deep learning model significantly outperformed all baseline models, achieving a coefficient of determination (R\\u0026sup2;) of 0.94\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.02 and a mean absolute error (MAE) of 2.13\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.32. Compared to the linear regression model, the deep learning approach improved R\\u0026sup2; by over 50% and reduced MAE by more than two-thirds.\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e\\u003cp\\u003eThis study demonstrates that an end-to-end deep learning framework can accurately and continuously track muscle fatigue directly from raw sEMG signals during high-intensity cycling. These findings highlight the superiority of deep learning over traditional feature-based linear models and provide a promising tool for real-time, individualized fatigue monitoring in elite sports performance.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Real-Time Continuous Assessment of Fatigue from Surface Electromyography with Deep Learning for Training Load Regulation in Elite Cyclists\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-11-17 23:45:09\",\"doi\":\"10.21203/rs.3.rs-7863940/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-02-20T07:24:35+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-02-17T13:51:55+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"183785492632374498902036342568690131914\",\"date\":\"2026-02-06T10:09:58+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-01-04T17:14:00+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"286761524632667779052840383037905063303\",\"date\":\"2025-12-12T10:17:10+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2025-11-04T09:06:39+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"334806195770286656303554598637221624191\",\"date\":\"2025-10-25T01:00:47+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"164313255189390283334771886352293108305\",\"date\":\"2025-10-23T13:27:36+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"242733132555699692353008834310285383440\",\"date\":\"2025-10-22T11:26:08+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"65917702306023904678070560645107213513\",\"date\":\"2025-10-20T11:02:23+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"301767320399794764267645088946818999080\",\"date\":\"2025-10-20T07:05:04+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2025-10-20T06:56:31+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2025-10-17T00:39:44+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2025-10-17T00:38:34+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Sports Science, Medicine and Rehabilitation\",\"date\":\"2025-10-15T05:13:59+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-sports-science-medicine-and-rehabilitation\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"ssmr\",\"sideBox\":\"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/ssmr/default.aspx\",\"title\":\"BMC Sports Science, Medicine and Rehabilitation\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"308ab33a-de81-40d0-a732-0de3892589f1\",\"owner\":[],\"postedDate\":\"November 17th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-17T05:38:20+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-11-17 23:45:09\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7863940\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7863940\",\"identity\":\"rs-7863940\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}