A 3D Convolutional Neural Network Model for Figure Skating Action Capture and Pose Recognition Based on Spatiotemporal Geometric Theory

A 3D Convolutional Neural Network Model for Figure Skating Action Capture and Pose Recognition Based on Spatiotemporal Geometric Theory | 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 A 3D Convolutional Neural Network Model for Figure Skating Action Capture and Pose Recognition Based on Spatiotemporal Geometric Theory Yueru Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8033131/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 This research addresses the challenging problem of automatic recognition of complex action sequences in figure skating by proposing a specialized 3D convolutional neural network model. First, based on Riemannian geometric theory, we establish a spatiotemporal manifold representation for figure skating actions, derive action invariance theorems, and construct adaptive spatiotemporal convolutional architectures with innovative attention mechanisms tailored for high-speed rotational movements. Second, we design precise capture algorithms for complex rotational, jumping, and artistic performance actions, achieving high-precision motion trajectory extraction. Third, we establish a hierarchical recognition mechanism for technical actions and error detection algorithms based on group-theoretic symmetry, completing multi-level recognition from basic postures to complete routines. Finally, through model accuracy validation, generalization performance testing, and computational efficiency optimization, we establish a comprehensive performance evaluation system. Experimental results demonstrate that the model achieves 94.7% accuracy in standard figure skating action recognition tasks and 91.2% precision in complex jumping action recognition, representing improvements of 12.3% and 15.8% respectively compared to traditional methods. Theoretical analysis proves the algorithm's convergence properties and generalization error bounds, providing important theoretical support and technical foundation for intelligent sports training and automated competition judging, with significant theoretical value and application prospects. action capture figure skating pose recognition 3D convolutional neural network 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-8033131","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":562048820,"identity":"a250b0a7-2754-4053-a9fc-e12ec2981978","order_by":0,"name":"Yueru 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First, based on Riemannian geometric theory, we establish a spatiotemporal manifold representation for figure skating actions, derive action invariance theorems, and construct adaptive spatiotemporal convolutional architectures with innovative attention mechanisms tailored for high-speed rotational movements. Second, we design precise capture algorithms for complex rotational, jumping, and artistic performance actions, achieving high-precision motion trajectory extraction. Third, we establish a hierarchical recognition mechanism for technical actions and error detection algorithms based on group-theoretic symmetry, completing multi-level recognition from basic postures to complete routines. Finally, through model accuracy validation, generalization performance testing, and computational efficiency optimization, we establish a comprehensive performance evaluation system. 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