ResNet50-Enhanced Multi-Layer Perceptron with Logistic Regression for Precise Martial Arts Action Recognition

ResNet50-Enhanced Multi-Layer Perceptron with Logistic Regression for Precise Martial Arts Action Recognition | 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 Short Report ResNet50-Enhanced Multi-Layer Perceptron with Logistic Regression for Precise Martial Arts Action Recognition Junhua Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8477528/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 14 You are reading this latest preprint version Abstract Martial arts integrate physical training, mental resilience, personal growth, social bonding and cultural heritage, offering a holistic wellness solution for modern life. Accurate recognition of martial arts techniques is critical to ensure safety, optimize athletic performance and promote cultural understanding, while misrecognition may cause avoidable injuries, aggressive conduct and biased cultural perceptions. Despite existing action recognition methods for martial arts, challenges like weak environmental robustness and limited generalizability remain unsolved. To address these gaps, this study proposes a hybrid MLP-LR model enhanced by ResNet50 for feature extraction, using the U/M-FIS dataset (5,000+ annotated images, 10+ classes) as input. A rigorous preprocessing pipeline (resizing, normalization, augmentation, denoising, contrast enhancement) is adopted to improve data quality. ResNet50 extracts discriminative features to reduce dimensionality and enhance anti-noise ability, and the MLP-LR classifier performs action classification after dataset splitting. Experimental results show the proposed model achieves superior performance: 99.96% accuracy, 99.87% precision, 99.52% recall and 99.47% F1-score, outperforming traditional CNN methods. This work provides a reliable and efficient framework for martial arts action recognition, with promising applications in sports training, athlete performance analysis and cultural heritage preservation. Martial arts action recognition ResNet50 Multi-Layer Perceptron Logistic Regression Image preprocessing Sports analytics Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 13 Mar, 2026 Reviews received at journal 10 Mar, 2026 Reviewers agreed at journal 04 Mar, 2026 Reviews received at journal 24 Feb, 2026 Reviewers agreed at journal 28 Jan, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviewers agreed at journal 27 Jan, 2026 Reviews received at journal 26 Jan, 2026 Reviewers agreed at journal 23 Jan, 2026 Reviewers agreed at journal 23 Jan, 2026 Reviewers invited by journal 22 Jan, 2026 Editor assigned by journal 18 Jan, 2026 Submission checks completed at journal 17 Jan, 2026 First submitted to journal 17 Jan, 2026 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-8477528","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":580509218,"identity":"6965331e-9844-46e3-b52f-f3f9e5825463","order_by":0,"name":"Junhua Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYBACPoYzDAc+/rORY2NvP0CcFjaGM4wPZ7ClGfPxnEkgVgsPszEP26HEeRIOBkRqYTx7THIGz4H0NgmGBIYfFduIseVcmsQHiTu5bdKNBxh7ztwmRssZM8kZBs9y22QOJDAzthGpRZon4XA6m0SCAdFajI15DhxOIEmL4cOZDWmGbcBAPkiUX/glzhgc+NhgIy/f3n7wwY8KIrQwSBxAsA/gUoRmTQNx6kbBKBgFo2AEAwD8ej4FwAnXywAAAABJRU5ErkJggg==","orcid":"","institution":"China Women's University","correspondingAuthor":true,"prefix":"","firstName":"Junhua","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-12-30 05:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8477528/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8477528/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101298042,"identity":"db8b6430-70f1-4c90-aeaf-7c1c3e0efd17","added_by":"auto","created_at":"2026-01-28 09:29:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":509091,"visible":true,"origin":"","legend":"","description":"","filename":"ResNet50EnhancedMultiLayerPerceptronwithLogisticRegressionforPreciseMartialArtsActionRecognition.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8477528/v1_covered_b5ddd900-6aea-4bdd-b053-33a64d2e71a1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"ResNet50-Enhanced Multi-Layer Perceptron with Logistic Regression for Precise Martial Arts Action Recognition","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":"discover-computing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Computing](https://link.springer.com/journal/10791)","snPcode":"10791","submissionUrl":"https://submission.springernature.com/new-submission/10791/3","title":"Discover Computing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Martial arts action recognition, ResNet50, Multi-Layer Perceptron, Logistic Regression, Image preprocessing, Sports analytics","lastPublishedDoi":"10.21203/rs.3.rs-8477528/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8477528/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Martial arts integrate physical training, mental resilience, personal growth, social bonding and cultural heritage, offering a holistic wellness solution for modern life. 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