Real-Time Fall Detection in Clinical and HomeEnvironments Using YOLO-Based PoseEstimation and Spatio-Temporal Skeletal Features

Real-Time Fall Detection in Clinical and HomeEnvironments Using YOLO-Based PoseEstimation and Spatio-Temporal Skeletal Features | 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 Real-Time Fall Detection in Clinical and HomeEnvironments Using YOLO-Based PoseEstimation and Spatio-Temporal Skeletal Features Houssein Taleb, Mostafa Rizk, Chamseddine Zaki, Jad Abou Chaaya, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9151996/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 15 You are reading this latest preprint version Abstract Falls have continued to pose a significant risk, particularly for the elderly. Preventing injuries and fatalities has required accurate and timely detection. However, the complexity of real-world environments and the need for precision have presented ongoing challenges to existing fall detection systems. While wear-able sensors have proven useful, they are often uncomfortable for continuous use, and traditional detection methods have demonstrated unreliability due to their sensitivity to environmental conditions. Consequently, the development of a more accurate, real-time, non-invasive, and environment-independent detection 1 approach has become essential. In this study, we have developed and evaluated two novel vision-based fall detection systems. In the first system, we have employed You Only Look Once , version 8 (YOLOv8) or YOLOv11 for real-time detection of both the person and the bed within each video frame. Subsequently, we have applied AlphaPose to extract human body keypoints, followed by action recognition using Spatial-Temporal Graph Convolutional Networks (ST-GCN). A custom fall detection logic has been integrated, which evaluates both posture and spatial position relative to the bed to confirm fall events. In the second system , we have utilized pose-based models (YOLOv8-pose or YOLOv11-pose) that simultaneously detect the person and estimate keypoints. Based on this data, we have designed an independent fall logic that classifies fall events through posture and location analysis. This system has also incorporated a real-time alert mechanism that sends WhatsApp notifications to enable immediate response in the event of a fall. Experimental results have demonstrated that both systems offer robust and reliable fall detection across various scenarios, significantly enhancing safety and supporting the well-being of individuals at risk. AlphaPose Computer Vision Elderly Care Fall Detection Fall Logic Fall Alarm System ST-GCN YOLOv8 YOLOv11 YOLOv8-pose YOLOv11-pose Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 04 May, 2026 Reviews received at journal 17 Apr, 2026 Reviews received at journal 17 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviews received at journal 14 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers agreed at journal 09 Apr, 2026 Reviewers invited by journal 09 Apr, 2026 Editor invited by journal 03 Apr, 2026 Editor assigned by journal 28 Mar, 2026 Submission checks completed at journal 28 Mar, 2026 First submitted to journal 17 Mar, 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. 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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-9151996","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":623123107,"identity":"d30bc3a7-887f-4c40-a11f-5c8d6e440a32","order_by":0,"name":"Houssein Taleb","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFElEQVRIiWNgGAWjYBACAwglASIYDzA22ADpAwwMPAwMMgYEtbAB1TI2pMG18BDQwgDTchjCwafFnL3H+OOPCos8BvnmBwc+7jifOL/xAOODt20MPOY4tFj2nDGT5jkjUczAxmZwcOaZ24kbDhxgNpwL1GLZgMNhN3LMmBnbJBIb2BgMDvO2AbUwHGCT5gVqMTiAU4vxx5//QFrYPxz+23YucX7DAfbfBLQYSPA2gLTwGBxmbDuQ2HDgABszXi1njpVJ8xyTSGxjyyk42Hsm2XjDgYPNknPOSeD2y/HmzR9/1NQl9jMf3/jg5w472fkzDh/88KbMRg5XiMEBG5wlcRBkvAQhDciAH4eDRsEoGAWjYMQCAKMJYP7cR5M2AAAAAElFTkSuQmCC","orcid":"","institution":"Saint Joseph University","correspondingAuthor":true,"prefix":"","firstName":"Houssein","middleName":"","lastName":"Taleb","suffix":""},{"id":623123110,"identity":"bc31dc46-ce82-4f78-a470-ec90035ae3f8","order_by":1,"name":"Mostafa Rizk","email":"","orcid":"","institution":"Lebanese American University","correspondingAuthor":false,"prefix":"","firstName":"Mostafa","middleName":"","lastName":"Rizk","suffix":""},{"id":623123118,"identity":"3d3f0440-8dfc-47ce-8125-23e0bbb33d6c","order_by":2,"name":"Chamseddine Zaki","email":"","orcid":"","institution":"American University of the Middle East","correspondingAuthor":false,"prefix":"","firstName":"Chamseddine","middleName":"","lastName":"Zaki","suffix":""},{"id":623123121,"identity":"0e0aa0d8-ff06-4fb5-a3d7-28fdd2b5636a","order_by":3,"name":"Jad Abou Chaaya","email":"","orcid":"","institution":"Brest National Engineering School","correspondingAuthor":false,"prefix":"","firstName":"Jad","middleName":"Abou","lastName":"Chaaya","suffix":""},{"id":623123122,"identity":"ecdbeddd-1910-435d-a6e8-a48152fb2fe7","order_by":4,"name":"Abbass Nasser","email":"","orcid":"","institution":"Holy Spirit University of Kaslik","correspondingAuthor":false,"prefix":"","firstName":"Abbass","middleName":"","lastName":"Nasser","suffix":""}],"badges":[],"createdAt":"2026-03-17 19:09:23","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9151996/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9151996/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107481863,"identity":"9d342736-0292-4560-be11-5fbf84bc06f0","added_by":"auto","created_at":"2026-04-22 02:20:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1138846,"visible":true,"origin":"","legend":"","description":"","filename":"SpringerFallDetectionUsingPoseEstimation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9151996/v1_covered_a480bf91-d7b9-4309-9679-6bb6fa611c79.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Real-Time Fall Detection in Clinical and HomeEnvironments Using YOLO-Based PoseEstimation and Spatio-Temporal Skeletal Features","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":"AlphaPose, Computer Vision, Elderly Care, Fall Detection, Fall Logic, Fall Alarm System, ST-GCN, YOLOv8, YOLOv11, YOLOv8-pose, YOLOv11-pose","lastPublishedDoi":"10.21203/rs.3.rs-9151996/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9151996/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Falls have continued to pose a significant risk, particularly for the elderly. 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