{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/vision-based-fallen-person-detection-for-the","title":"Vision-Based Fallen Person Detection for the Elderly","arxiv_id":"1707.07608","date":"2017-07-24","proceeding":null,"authors":["Markus D. Solbach","John K. Tsotsos"],"abstract":"Falls are serious and costly for elderly people. The Centers for Disease\nControl and Prevention of the US reports that millions of older people, 65 and\nolder, fall each year at least once. Serious injuries such as; hip fractures,\nbroken bones or head injury, are caused by 20% of the falls. The time it takes\nto respond and treat a fallen person is crucial. With this paper we present a\nnew , non-invasive system for fallen people detection. Our approach uses only\nstereo camera data for passively sensing the environment. The key novelty is a\nhuman fall detector which uses a CNN based human pose estimator in combination\nwith stereo data to reconstruct the human pose in 3D and estimate the ground\nplane in 3D. Furthermore, our system consists of a reasoning module which\nformulates a number of measures to reason whether a person is fallen. We have\ntested our approach in different scenarios covering most activities elderly\npeople might encounter living at home. Based on our extensive evaluations, our\nsystems shows high accuracy and almost no miss-classification. To reproduce our\nresults, the implementation is publicly available to the scientific community.","url_abs":"http://arxiv.org/abs/1707.07608v2","url_pdf":"http://arxiv.org/pdf/1707.07608v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"vision-based-fallen-person-detection-for-the","repo_url":"https://github.com/TsotsosLab/fallen-person-detector","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"human-detection","task_name":"Human Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1707.07608","atlas_url":"https://app.syntology.ai/?focus=1707.07608","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}