Involution Feature Extraction Network Based Human Posture Recognition in Martial Arts Movement Recognition

Involution Feature Extraction Network Based Human Posture Recognition in Martial Arts Movement 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 Research Article Involution Feature Extraction Network Based Human Posture Recognition in Martial Arts Movement Recognition Sifang Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3977431/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 With the development of computers in recent years, human body recognition technology has been vigorously developed and is widely used in motion analysis, video surveillance and other fields. As the traditional human action recognition relies on video decomposition frame-by-frame, artificially designed motion features to achieve the role of recognition, this approach is both energy-consuming recognition efficiency is also very low. Thanks to the advent of deep learning, computers can automatically extract features from movements and then recognize and classify them. This research is based on deep learning to improve human pose estimation. Firstly, Involution's feature extraction network is proposed for lightweight human pose estimation, which is combined with existing human pose estimation models to recognize human pose. Each joint of the human body is labelled and classified, weights are added to each part, features are extracted between the joints at each moment, and the extracted features are then fed into a long and short term memory neural network for recognition. Experimental results show that the number of parameters and computational effort of the improved human pose estimation model is reduced by around 40% compared to the original model, while still providing a slight improvement in accuracy. The performance of the model under each algorithm is compared with the model proposed in this study, and the results show that the proposed model has better performance in recognizing different martial arts movements. human action recognition deep learning long- and short-term memory neural networks lightweight networks feature extraction 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. 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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-3977431","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":276443625,"identity":"aca9c8e7-54dc-4089-988c-0ade877ba609","order_by":0,"name":"Sifang Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIie3PsWoCQRDG8TkGxmbDtSOC+woDgUBAfJY9hKtMb0jhgrAWIql9CyuxPFw4mxNrIYUipDZYBSxyfcLdpbPYf70/5luAUOgeQ/TH9q03tugzMI1Ii1J5tmlkKTUNSayEX+2mJENptktPQOSwzlA7db2c1n2Ip7PqY9EEzGlRfJDkDytOigFwsVtWEkTIHpk+lej5ChKHIPxSTQgj27mR53LY+ZK4cT1R5RluOy+QK+DE+XrCSCRcpEZyeirJVtX+Rb/vr0ce9Yx2eP76dm/deDqvJr+X/u95KBQKhf7sBzN3RWVmjDR/AAAAAElFTkSuQmCC","orcid":"","institution":"Wuhan Sports University","correspondingAuthor":true,"prefix":"","firstName":"Sifang","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-02-22 03:39:47","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3977431/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3977431/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":53737273,"identity":"367f4043-5e7e-4c74-82a2-c5123f4773f2","added_by":"auto","created_at":"2024-03-29 14:46:56","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":312412,"visible":true,"origin":"","legend":"","description":"","filename":"Manuscript2.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3977431/v1_covered_c9eca6ac-4c05-4074-ab8a-6a1a9b755514.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Involution Feature Extraction Network Based Human Posture Recognition in Martial Arts Movement Recognition","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":"human action recognition, deep learning, long- and short-term memory neural networks, lightweight networks, feature extraction","lastPublishedDoi":"10.21203/rs.3.rs-3977431/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3977431/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eWith the development of computers in recent years, human body recognition technology has been vigorously developed and is widely used in motion analysis, video surveillance and other fields. 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Experimental results show that the number of parameters and computational effort of the improved human pose estimation model is reduced by around 40% compared to the original model, while still providing a slight improvement in accuracy. 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