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Review
. 2021 Feb 1;21(3):947.
doi: 10.3390/s21030947.

Comprehensive Review of Vision-Based Fall Detection Systems

Jesús Gutiérrez  1 , Víctor Rodríguez  2 , Sergio Martin  1
Affiliations
Review

Comprehensive Review of Vision-Based Fall Detection Systems

Jesús Gutiérrez et al. Sensors (Basel). .

Abstract

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.

Keywords: artificial vision; fall characterization; fall classification; fall dataset; fall detection; neural networks.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Flow diagram of adopted search and selection strategy for paper selection.
Figure 2
Figure 2
Typical convolutional neural network (CNN) architecture.
Figure 3
Figure 3
Convolutional pose machine presentation.
Figure 4
Figure 4
Support vector machine boundary definition.
See this image and copyright information in PMC

References

    1. United Nations . World Population Ageing 2017: Highlights. Department of Economic and Social Affairs, United Nations; New York, NY, USA: 2017.
    1. Sterling D.A., O’connor J.A., Bonadies J. Geriatric falls: Injury severity is high and disproportionate to mechanism. J. Trauma Inj. Infect. Crit. Care. 2001;50:116–119. doi: 10.1097/00005373-200101000-00021. - DOI - PubMed
    1. Vallabh P., Malekian R. Fall detection monitoring systems: A comprehensive review. J. Ambient. Intell. Humaniz. Comput. 2018;9:1809–1833. doi: 10.1007/s12652-017-0592-3. - DOI
    1. Rucco R., Sorriso A., Liparoti M., Ferraioli G., Sorrentino P., Ambrosanio M., Baselice F. Type and Location of Wearable Sensors for Monitoring Falls during Static and Dynamic Tasks in Healthy Elderly: A Review. Sensors. 2018;18:1613. doi: 10.3390/s18051613. - DOI - PMC - PubMed
    1. Yajai A., Rodtook A., Chinnasarn K., Rasmequan S., Apichet Y. Fall detection using directional bounding box; Proceedings of the 2015 12th International Joint Conference on Computer Science and Software Engineering (JCSSE); Hatyai, Thailand. 22–24 July 2015; pp. 52–57.

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