{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,7]],"date-time":"2026-05-07T16:11:26Z","timestamp":1778170286875,"version":"3.51.4"},"reference-count":60,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T00:00:00Z","timestamp":1625529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Falls are one of the most critical health care risks for elderly people, being, in some adverse circumstances, an indirect cause of death. Furthermore, demographic forecasts for the future show a growing elderly population worldwide. In this context, models for automatic fall detection and prediction are of paramount relevance, especially AI applications that use ambient, sensors or computer vision. In this paper, we present an approach for fall detection using computer vision techniques. Video sequences of a person in a closed environment are used as inputs to our algorithm. In our approach, we first apply the V2V-PoseNet model to detect 2D body skeleton in every frame. Specifically, our approach involves four steps: (1) the body skeleton is detected by V2V-PoseNet in each frame; (2) joints of skeleton are first mapped into the Riemannian manifold of positive semidefinite matrices of fixed-rank 2 to build time-parameterized trajectories; (3) a temporal warping is performed on the trajectories, providing a (dis-)similarity measure between them; (4) finally, a pairwise proximity function SVM is used to classify them into fall or non-fall, incorporating the (dis-)similarity measure into the kernel function. We evaluated our approach on two publicly available datasets URFD and Charfi. The results of the proposed approach are competitive with respect to state-of-the-art methods, while only involving 2D body skeletons.<\/jats:p>","DOI":"10.3390\/jimaging7070109","type":"journal-article","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T11:36:44Z","timestamp":1625571404000},"page":"109","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Fall Detection of Elderly People Using the Manifold of Positive Semidefinite Matrices"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8363-2753","authenticated-orcid":false,"given":"Abdessamad","family":"Youssfi Alaoui","sequence":"first","affiliation":[{"name":"ADMIR Laboratory, Rabat IT Center, IRDATeam, ENSIAS, Mohammed V University in Rabat, Rabat 10000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7030-3910","authenticated-orcid":false,"given":"Youness","family":"Tabii","sequence":"additional","affiliation":[{"name":"ADMIR Laboratory, Rabat IT Center, IRDATeam, ENSIAS, Mohammed V University in Rabat, Rabat 10000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9736-7260","authenticated-orcid":false,"given":"Rachid","family":"Oulad Haj Thami","sequence":"additional","affiliation":[{"name":"ADMIR Laboratory, Rabat IT Center, IRDATeam, ENSIAS, Mohammed V University in Rabat, Rabat 10000, Morocco"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4219-7860","authenticated-orcid":false,"given":"Mohamed","family":"Daoudi","sequence":"additional","affiliation":[{"name":"MT Lille Douai, Institut Mines-T\u00e9l\u00e9com, Centre for Digital Systems, F-59000 Lille, France"},{"name":"CNRS, Centrale Lille, Institut Mines-T\u00e9l\u00e9com, UMR 9189 CRIStAL, University Lille, F-59000 Lille, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1219-4386","authenticated-orcid":false,"given":"Stefano","family":"Berretti","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50121 Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5670-3774","authenticated-orcid":false,"given":"Pietro","family":"Pala","sequence":"additional","affiliation":[{"name":"Department of Information Engineering, University of Florence, 50121 Florence, Italy"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,6]]},"reference":[{"key":"ref_1","unstructured":"United Nations, Department of Economic and Social Affairs, Population Division (2021, June 29). 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