000099771 001__ 99771 000099771 005__ 20230519145508.0 000099771 0247_ $$2doi$$a10.3390/s21030947 000099771 0248_ $$2sideral$$a123248 000099771 037__ $$aART-2021-123248 000099771 041__ $$aeng 000099771 100__ $$aGutiérrez, J. 000099771 245__ $$aComprehensive review of vision-based fall detection systems 000099771 260__ $$c2021 000099771 5060_ $$aAccess copy available to the general public$$fUnrestricted 000099771 5203_ $$aVision-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. 000099771 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000099771 590__ $$a3.847$$b2021 000099771 592__ $$a0.803$$b2021 000099771 594__ $$a6.4$$b2021 000099771 591__ $$aCHEMISTRY, ANALYTICAL$$b29 / 87 = 0.333$$c2021$$dQ2$$eT2 000099771 593__ $$aAnalytical Chemistry$$c2021$$dQ1 000099771 591__ $$aINSTRUMENTS & INSTRUMENTATION$$b19 / 64 = 0.297$$c2021$$dQ2$$eT1 000099771 593__ $$aBiochemistry$$c2021$$dQ1 000099771 591__ $$aENGINEERING, ELECTRICAL & ELECTRONIC$$b95 / 277 = 0.343$$c2021$$dQ2$$eT2 000099771 593__ $$aInstrumentation$$c2021$$dQ1 000099771 593__ $$aInformation Systems$$c2021$$dQ1 000099771 593__ $$aElectrical and Electronic Engineering$$c2021$$dQ1 000099771 655_4 $$ainfo:eu-repo/semantics/review$$vinfo:eu-repo/semantics/publishedVersion 000099771 700__ $$0(orcid)0000-0002-6963-0727$$aRodríguez, V. 000099771 700__ $$aMartin, S. 000099771 773__ $$g21, 3 (2021), 947 [50 pp]$$pSensors$$tSensors$$x1424-8220 000099771 8564_ $$s1099676$$uhttps://zaguan.unizar.es/record/99771/files/texto_completo.pdf$$yVersión publicada 000099771 8564_ $$s2700386$$uhttps://zaguan.unizar.es/record/99771/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000099771 909CO $$ooai:zaguan.unizar.es:99771$$particulos$$pdriver 000099771 951__ $$a2023-05-18-15:09:03 000099771 980__ $$aARTICLE