{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T10:36:40Z","timestamp":1781606200981,"version":"3.54.5"},"reference-count":149,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T00:00:00Z","timestamp":1612137600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Vision-based fall detection systems have experienced fast development over the last years. To determine the course of its evolution and help new researchers, the main audience of this paper, a comprehensive revision of all published articles in the main scientific databases regarding this area during the last five years has been made. After a selection process, detailed in the Materials and Methods Section, eighty-one systems were thoroughly reviewed. Their characterization and classification techniques were analyzed and categorized. Their performance data were also studied, and comparisons were made to determine which classifying methods best work in this field. The evolution of artificial vision technology, very positively influenced by the incorporation of artificial neural networks, has allowed fall characterization to become more resistant to noise resultant from illumination phenomena or occlusion. The classification has also taken advantage of these networks, and the field starts using robots to make these systems mobile. However, datasets used to train them lack real-world data, raising doubts about their performances facing real elderly falls. In addition, there is no evidence of strong connections between the elderly and the communities of researchers.<\/jats:p>","DOI":"10.3390\/s21030947","type":"journal-article","created":{"date-parts":[[2021,2,1]],"date-time":"2021-02-01T03:49:48Z","timestamp":1612151388000},"page":"947","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":134,"title":["Comprehensive Review of Vision-Based Fall Detection Systems"],"prefix":"10.3390","volume":"21","author":[{"given":"Jes\u00fas","family":"Guti\u00e9rrez","sequence":"first","affiliation":[{"name":"Universidad Nacional de Educaci\u00f3n a Distancia, Juan Rosal 12, 28040 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"V\u00edctor","family":"Rodr\u00edguez","sequence":"additional","affiliation":[{"name":"EduQTech, E.U. Polit\u00e9cnica, Maria Lluna 3, 50018 Zaragoza, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4118-0234","authenticated-orcid":false,"given":"Sergio","family":"Martin","sequence":"additional","affiliation":[{"name":"Universidad Nacional de Educaci\u00f3n a Distancia, Juan Rosal 12, 28040 Madrid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,2,1]]},"reference":[{"key":"ref_1","unstructured":"United Nations (2017). World Population Ageing 2017: Highlights, Department of Economic and Social Affairs, United Nations."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"116","DOI":"10.1097\/00005373-200101000-00021","article-title":"Geriatric falls: Injury severity is high and disproportionate to mechanism","volume":"50","author":"Sterling","year":"2001","journal-title":"J. Trauma Inj. Infect. Crit. 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