A method for detecting key points of back acupoints based on deep learning

A method for detecting key points of back acupoints based on deep learning | 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 method for detecting key points of back acupoints based on deep learning Chengjun Tian, Guangqiang Song, Yang Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6837608/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Mar, 2026 Read the published version in Multimedia Systems → Version 1 posted 10 You are reading this latest preprint version Abstract Addressing the limitations of subjective experience in traditional Chinese medicine back acupoint localization and the insufficient accuracy of automated identification, this paper introduces a lightweight multi-network fusion model. We construct a dual-backbone architecture featuring MobileNetV2-HRNet. MobileNetV2 is employed for efficient feature extraction, followed by HRNet's parallel multi-branch structure to achieve multi-resolution feature fusion, integrating both local details and global structural information. Furthermore, a channel-spatial dual-dimension attention mechanism dynamically focuses on crucial regions. We also design an anatomical constraint hybrid loss function. The model utilizes heatmap regression to pinpoint acupoint coordinates, ultimately achieving precise localization of 13 back acupoints. Our method achieves an average detection accuracy of 92.3% on our self-built back acupoint dataset, demonstrating a 24.6% improvement over traditional image processing techniques. With a parameter size of only 4.2M and a single-frame inference time of under 15ms, this model holds significant application potential in health monitoring and related fields. Key point detection MobileNetV2 HRNet image processing lightweight Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 10 Mar, 2026 Read the published version in Multimedia Systems → Version 1 posted Editorial decision: Revision requested 25 Nov, 2025 Reviews received at journal 24 Nov, 2025 Reviewers agreed at journal 24 Nov, 2025 Reviews received at journal 22 Oct, 2025 Reviewers agreed at journal 19 Oct, 2025 Reviewers agreed at journal 14 Oct, 2025 Reviewers invited by journal 02 Aug, 2025 Editor assigned by journal 25 Jun, 2025 Submission checks completed at journal 09 Jun, 2025 First submitted to journal 06 Jun, 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-6837608","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":494790543,"identity":"e7e1784e-72fe-4f85-bb0d-bd0873fdccb2","order_by":0,"name":"Chengjun Tian","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAUlEQVRIiWNgGAWjYDACZiBmbACxDjAwfIAKSuDTwYOshXEGSISgFga4FqCNPMRosWdnfvbg5w6bPHnHM2aPbWoO29szMB+8zcNgl4fbYWzmhr1n0ooND5wxN845djixh4Et2ZqHIbkYj1/MpBnbDidubDhjJp3bcDiBh4HHTJqH4UBiA04t7N+AWv5DtFg2HLbnYeD/RkALD8iWA4nzGYBaGBsOM/Yw8LDh13KYp0yyty05cQPDsTLJnmPpiT2H2Ywt5xgk49TC3n98m8TPNrvE+TMOb5P4UWNtz97e/PDGmwo7nFrgwODGASiLGcwlpB4I5PsJmjoKRsEoGAUjFQAA7ZRP56/+obgAAAAASUVORK5CYII=","orcid":"","institution":"Changchun University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Chengjun","middleName":"","lastName":"Tian","suffix":""},{"id":494790544,"identity":"da801cad-571b-4e93-ad67-0d0c5c03d330","order_by":1,"name":"Guangqiang Song","email":"","orcid":"","institution":"Changchun University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Guangqiang","middleName":"","lastName":"Song","suffix":""},{"id":494790545,"identity":"93ea10c6-c150-4417-99be-c88857e6d5a2","order_by":2,"name":"Yang Li","email":"","orcid":"","institution":"Changchun University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2025-06-06 13:53:28","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6837608/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6837608/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s00530-026-02270-5","type":"published","date":"2026-03-10T15:59:33+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":104739945,"identity":"f702dd02-4859-4f89-8306-cdc2d85ff9b0","added_by":"auto","created_at":"2026-03-16 16:13:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":926158,"visible":true,"origin":"","legend":"","description":"","filename":"Amethodfordetectingkeypointsofbackacupoint.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6837608/v1_covered_113eee40-22e3-403a-bea9-b973b65340bf.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"A method for detecting key points of back acupoints based on deep learning","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"multimedia-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mmsj","sideBox":"Learn more about [Multimedia Systems](http://link.springer.com/journal/530)","snPcode":"530","submissionUrl":"https://submission.nature.com/new-submission/530/3","title":"Multimedia Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Key point detection, MobileNetV2, HRNet, image processing, lightweight","lastPublishedDoi":"10.21203/rs.3.rs-6837608/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6837608/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e Addressing the limitations of subjective experience in traditional Chinese medicine back acupoint localization and the insufficient accuracy of automated identification, this paper introduces a lightweight multi-network fusion model. 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