Enhanced Human Fall Detection via Lightweight MDS-OpenPose Framework

Enhanced Human Fall Detection via Lightweight MDS-OpenPose Framework | 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 Enhanced Human Fall Detection via Lightweight MDS-OpenPose Framework Di Wang, Gangyang Nan, Fang Xia This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6343415/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Falls among the elderly pose a significant risk of injury and even mortality, underscoring the importance of real-time monitoring systems to mitigate these hazards. Existing posture estimation-based fall detection methods often struggle with high parameter counts, computational complexity, and slow processing speeds. This paper proposes an improved OpenPose algorithm, termed MDS-OpenPose, which addresses these issues. By integrating the lightweight MobileNetV3 network to replace the original VGG feature extraction network, optimizing convolutional layer sizes, and introducing DenseNet dense connections, MDS-OpenPose significantly reduces model complexity while maintaining high accuracy. Fall detection is achieved through a comprehensive method that analyzes vertical distances between the head and feet, trunk tilt angles, and horizontal displacement of the center of mass. Experimental results demonstrate that MDS-OpenPose achieves a substantial improvement in FPS on the COCO dataset while maintaining high precision and recall rates. On the Fall Down dataset, it attains an accuracy of 93.0% and a precision of 92.1%, showcasing its effectiveness and robustness in real-time fall detection applications. Fall Detection Pose Estimation OpenPose MobileNetV Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 22 May, 2025 Reviews received at journal 19 May, 2025 Reviews received at journal 07 May, 2025 Reviewers agreed at journal 03 May, 2025 Reviewers agreed at journal 02 May, 2025 Reviewers agreed at journal 02 May, 2025 Reviewers agreed at journal 30 Apr, 2025 Reviewers agreed at journal 30 Apr, 2025 Reviewers agreed at journal 30 Apr, 2025 Reviewers invited by journal 29 Apr, 2025 Editor assigned by journal 29 Apr, 2025 Submission checks completed at journal 05 Apr, 2025 First submitted to journal 31 Mar, 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. 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-6343415","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":451587619,"identity":"259dcd6a-32a5-45d7-8144-9a6e334f6960","order_by":0,"name":"Di Wang","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)","correspondingAuthor":false,"prefix":"","firstName":"Di","middleName":"","lastName":"Wang","suffix":""},{"id":451587620,"identity":"f2ad6d2a-714b-49ea-8f0c-3af9dac2b7a0","order_by":1,"name":"Gangyang Nan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYFCCxOYHif9s5PjlDx988MHAxo4ILcltBg/Y0owlZ7AlG84oSEsmQkt6g+QDtsOJG27wqEnzfDjE2EBIg8HxxAaDBJ60xJmze5iNbQwOMDOwHz66Aa+WMw8bHiRI2Bj3y5w9+DjH4A4fA09a2g18WsxugGwxSJOd2ZCXbJxj8IyZQYLHjKAWiYSEw4wbDuSYSVsYHGZsIE7LgcOKG24AtTAQo8X+zMM2g8QGYCD3HEs27DFIS2Yj5BfJ9vTHD382AKOSvfnggx9/bOz42Q8fw6sFE7CRpnwUjIJRMApGATYAAHPuVjw4RiiSAAAAAElFTkSuQmCC","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)","correspondingAuthor":true,"prefix":"","firstName":"Gangyang","middleName":"","lastName":"Nan","suffix":""},{"id":451587621,"identity":"21f3b419-ad83-4e8a-b73c-2a2cf24f03c4","order_by":2,"name":"Fang Xia","email":"","orcid":"","institution":"Qilu University of Technology (Shandong Academy of Sciences)","correspondingAuthor":false,"prefix":"","firstName":"Fang","middleName":"","lastName":"Xia","suffix":""}],"badges":[],"createdAt":"2025-03-31 09:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6343415/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6343415/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82350060,"identity":"56953e67-2c41-4ac9-ab43-bde1b11f86e9","added_by":"auto","created_at":"2025-05-09 10:50:21","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":635126,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6343415/v1_covered_085f162d-d24a-4d2e-8243-87d610c77fc2.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Enhanced Human Fall Detection via Lightweight MDS-OpenPose Framework","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":"the-journal-of-supercomputing","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [The Journal of Supercomputing](https://www.springer.com/journal/11227)","snPcode":"11227","submissionUrl":"https://submission.nature.com/new-submission/11227/3","title":"The Journal of Supercomputing","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Fall Detection, Pose Estimation, OpenPose, MobileNetV","lastPublishedDoi":"10.21203/rs.3.rs-6343415/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6343415/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Falls among the elderly pose a significant risk of injury and even mortality, underscoring the importance of real-time monitoring systems to mitigate these hazards. 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