{"paper_id":"4a4ad696-aed8-477f-9d97-c598bcd4b799","body_text":"Spatiotemporal Analysis for Accurate Real-Time Fall Identification in Elderly Care | 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 Spatiotemporal Analysis for Accurate Real-Time Fall Identification in Elderly Care Mishel Thomas Sony, Dr.Revathi B.S This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9231837/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Falls result in the most severe injuries in older adults. Data shows that more than one in five older adults experience at least one fall each year, and nearly one in three older adults fall annually. Almost half of older adults 42% over 70 years of age fall at least once each year. Being able to recognize falls quickly is very important. A quick response can help prevent serious complications from falls. This journal presents a real-time visual fall identification system based on YOLOv8, with the Yolo Model scoring system and recovery metrics to accurately identify falls without using any wearable sensors. This proposed framework utilizes several different analysis methods for identifying the different stages of falling, standing, sitting, lying down, and recovery. It does this by analyzing multiple spatio-temporal indicators such as height loss, bounding box aspect, displacement of body mass, and time of persistence between points in determining whether someone has fallen or appears to have fallen. The scoring system helps to provide a more accurate means of identifying a fall based on the spatiotemporal factors reduced rate of false alarms and improved accuracy of identifying elderly individuals before or after they fall, by using many different spatiotemporal features as opposed to using only a single one or threshold method. The developed system can operate in real-time using a webcam and provides visual alerts of detected falls with a method for providing confidence ranging from 0% to 100% to the viewer. The system developed in this work is capable of being used in elder care type facilities and for the wider implementation of smart surveillance systems that monitor areas where older adults frequently spend time. However, the reliability of detecting falls and post-fall recovery could be negatively affected due to severe occlusions, poor lighting conditions, or due to too many individuals in the same location. Despite these limitations, the framework offers improved interpretability and robustness compared to existing vision-based fall detection systems. Fall Detection YOLOv8 Real Time Monitoring Computer Vision Elderly Care Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 05 Apr, 2026 Reviewers invited by journal 05 Apr, 2026 Editor assigned by journal 30 Mar, 2026 Submission checks completed at journal 30 Mar, 2026 First submitted to journal 26 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. 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. 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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-9231837\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":619676809,\"identity\":\"fafa45b8-a004-447d-bcc4-91ac56a1698e\",\"order_by\":0,\"name\":\"Mishel Thomas Sony\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABFUlEQVRIiWNgGAWjYDACZgY2BoYChgQw58MBJHEQwq3FwACshXEGUVoYkLQw86BowQF029mfPfhg8CePX+zw4882Z+zyDW73GH5gqLBObGDnPYBNi9lhHnPDGQYGxZKz08ykc24kW264c8ZYguFMemIDM18CDi1s0jwGBokbbieYMed8YDYwuJFjIMHYdhiohccAuxb2Z9J/wFrSP3+2+FAP0mL8g/EfPi0MZtIMYC05BtIMNw6DtJhJMDbg08JjJtljYJw4c3ZOmWTPmeMGkjfSyiwSjqUbt+HScv74M4kfFXKJ/dLpmz/8OFZtwHcjefONDzXWsv38Z7BqwQY4DMCJgY1Y9UDA/oAExaNgFIyCUTACAABek2Bqm5FGEAAAAABJRU5ErkJggg==\",\"orcid\":\"\",\"institution\":\"Karunya Institute of Technology and Sciences\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Mishel\",\"middleName\":\"Thomas\",\"lastName\":\"Sony\",\"suffix\":\"\"},{\"id\":619676810,\"identity\":\"6b39b3f2-7762-4aa9-8db5-116baf29ff5d\",\"order_by\":1,\"name\":\"Dr.Revathi B.S\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Karunya Institute of Technology and Sciences\",\"correspondingAuthor\":false,\"prefix\":\"Dr.\",\"firstName\":\"Revathi\",\"middleName\":\"\",\"lastName\":\"B.S\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-03-26 09:09:22\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9231837/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9231837/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":106726111,\"identity\":\"e9e1ba55-7b0a-4139-aa5f-12ada32e78b5\",\"added_by\":\"auto\",\"created_at\":\"2026-04-12 18:35:22\",\"extension\":\"pdf\",\"order_by\":1,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":490457,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"MishelThomasSonyJournal.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9231837/v1_covered_378a6afc-b098-4d0e-b623-7956bc590b30.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Spatiotemporal Analysis for Accurate Real-Time Fall Identification in Elderly Care\",\"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\":\"info@researchsquare.com\",\"identity\":\"signal-image-and-video-processing\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"sivp\",\"sideBox\":\"Learn more about [Signal, Image and Video Processing](http://link.springer.com/journal/11760)\",\"snPcode\":\"11760\",\"submissionUrl\":\"https://submission.nature.com/new-submission/11760/3\",\"title\":\"Signal, Image and Video Processing\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Fall Detection, YOLOv8, Real Time Monitoring, Computer Vision, Elderly Care\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9231837/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9231837/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eFalls result in the most severe injuries in older adults. 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