Comprehensive Review of Vision-Based Fall Detection Systems
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 durin...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-02-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/21/3/947 |
| _version_ | 1827605153725808640 |
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| author | Jesús Gutiérrez Víctor Rodríguez Sergio Martin |
| author_facet | Jesús Gutiérrez Víctor Rodríguez Sergio Martin |
| author_sort | Jesús Gutiérrez |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-09T06:15:41Z |
| format | Article |
| id | doaj.art-78c3bcdd28ea4a4cbd0e3fbefe29672c |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T06:15:41Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-78c3bcdd28ea4a4cbd0e3fbefe29672c2023-12-03T11:53:08ZengMDPI AGSensors1424-82202021-02-0121394710.3390/s21030947Comprehensive Review of Vision-Based Fall Detection SystemsJesús Gutiérrez0Víctor Rodríguez1Sergio Martin2Universidad Nacional de Educación a Distancia, Juan Rosal 12, 28040 Madrid, SpainEduQTech, E.U. Politécnica, Maria Lluna 3, 50018 Zaragoza, SpainUniversidad Nacional de Educación a Distancia, Juan Rosal 12, 28040 Madrid, SpainVision-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.https://www.mdpi.com/1424-8220/21/3/947artificial visionneural networksfall detectionfall characterizationfall classificationfall dataset |
| spellingShingle | Jesús Gutiérrez Víctor Rodríguez Sergio Martin Comprehensive Review of Vision-Based Fall Detection Systems Sensors artificial vision neural networks fall detection fall characterization fall classification fall dataset |
| title | Comprehensive Review of Vision-Based Fall Detection Systems |
| title_full | Comprehensive Review of Vision-Based Fall Detection Systems |
| title_fullStr | Comprehensive Review of Vision-Based Fall Detection Systems |
| title_full_unstemmed | Comprehensive Review of Vision-Based Fall Detection Systems |
| title_short | Comprehensive Review of Vision-Based Fall Detection Systems |
| title_sort | comprehensive review of vision based fall detection systems |
| topic | artificial vision neural networks fall detection fall characterization fall classification fall dataset |
| url | https://www.mdpi.com/1424-8220/21/3/947 |
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