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Home Camera-Based Fall Detection System for the Elderly
Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we pres...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751723/ https://www.ncbi.nlm.nih.gov/pubmed/29232846 http://dx.doi.org/10.3390/s17122864 |
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author | de Miguel, Koldo Brunete, Alberto Hernando, Miguel Gambao, Ernesto |
author_facet | de Miguel, Koldo Brunete, Alberto Hernando, Miguel Gambao, Ernesto |
author_sort | de Miguel, Koldo |
collection | PubMed |
description | Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow) as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%. |
format | Online Article Text |
id | pubmed-5751723 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-57517232018-01-10 Home Camera-Based Fall Detection System for the Elderly de Miguel, Koldo Brunete, Alberto Hernando, Miguel Gambao, Ernesto Sensors (Basel) Article Falls are the leading cause of injury and death in elderly individuals. Unfortunately, fall detectors are typically based on wearable devices, and the elderly often forget to wear them. In addition, fall detectors based on artificial vision are not yet available on the market. In this paper, we present a new low-cost fall detector for smart homes based on artificial vision algorithms. Our detector combines several algorithms (background subtraction, Kalman filtering and optical flow) as input to a machine learning algorithm with high detection accuracy. Tests conducted on over 50 different fall videos have shown a detection ratio of greater than 96%. MDPI 2017-12-09 /pmc/articles/PMC5751723/ /pubmed/29232846 http://dx.doi.org/10.3390/s17122864 Text en © 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article de Miguel, Koldo Brunete, Alberto Hernando, Miguel Gambao, Ernesto Home Camera-Based Fall Detection System for the Elderly |
title | Home Camera-Based Fall Detection System for the Elderly |
title_full | Home Camera-Based Fall Detection System for the Elderly |
title_fullStr | Home Camera-Based Fall Detection System for the Elderly |
title_full_unstemmed | Home Camera-Based Fall Detection System for the Elderly |
title_short | Home Camera-Based Fall Detection System for the Elderly |
title_sort | home camera-based fall detection system for the elderly |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751723/ https://www.ncbi.nlm.nih.gov/pubmed/29232846 http://dx.doi.org/10.3390/s17122864 |
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