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Fall Detection Using Multiple Bioradars and Convolutional Neural Networks

A lack of effective non-contact methods for automatic fall detection, which may result in the development of health and life-threatening conditions, is a great problem of modern medicine, and in particular, geriatrics. The purpose of the present work was to investigate the advantages of utilizing a...

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Autores principales: Anishchenko, Lesya, Zhuravlev, Andrey, Chizh, Margarita
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960824/
https://www.ncbi.nlm.nih.gov/pubmed/31861061
http://dx.doi.org/10.3390/s19245569
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author Anishchenko, Lesya
Zhuravlev, Andrey
Chizh, Margarita
author_facet Anishchenko, Lesya
Zhuravlev, Andrey
Chizh, Margarita
author_sort Anishchenko, Lesya
collection PubMed
description A lack of effective non-contact methods for automatic fall detection, which may result in the development of health and life-threatening conditions, is a great problem of modern medicine, and in particular, geriatrics. The purpose of the present work was to investigate the advantages of utilizing a multi-bioradar system in the accuracy of remote fall detection. The proposed concept combined usage of wavelet transform and deep learning to detect fall episodes. The continuous wavelet transform was used to get a time-frequency representation of the bio-radar signal and use it as input data for a pre-trained convolutional neural network AlexNet adapted to solve the problem of detecting falls. Processing of the experimental results showed that the designed multi-bioradar system can be used as a simple and view-independent approach implementing a non-contact fall detection method with an accuracy and F1-score of 99%.
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spelling pubmed-69608242020-01-24 Fall Detection Using Multiple Bioradars and Convolutional Neural Networks Anishchenko, Lesya Zhuravlev, Andrey Chizh, Margarita Sensors (Basel) Article A lack of effective non-contact methods for automatic fall detection, which may result in the development of health and life-threatening conditions, is a great problem of modern medicine, and in particular, geriatrics. The purpose of the present work was to investigate the advantages of utilizing a multi-bioradar system in the accuracy of remote fall detection. The proposed concept combined usage of wavelet transform and deep learning to detect fall episodes. The continuous wavelet transform was used to get a time-frequency representation of the bio-radar signal and use it as input data for a pre-trained convolutional neural network AlexNet adapted to solve the problem of detecting falls. Processing of the experimental results showed that the designed multi-bioradar system can be used as a simple and view-independent approach implementing a non-contact fall detection method with an accuracy and F1-score of 99%. MDPI 2019-12-17 /pmc/articles/PMC6960824/ /pubmed/31861061 http://dx.doi.org/10.3390/s19245569 Text en © 2019 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
Anishchenko, Lesya
Zhuravlev, Andrey
Chizh, Margarita
Fall Detection Using Multiple Bioradars and Convolutional Neural Networks
title Fall Detection Using Multiple Bioradars and Convolutional Neural Networks
title_full Fall Detection Using Multiple Bioradars and Convolutional Neural Networks
title_fullStr Fall Detection Using Multiple Bioradars and Convolutional Neural Networks
title_full_unstemmed Fall Detection Using Multiple Bioradars and Convolutional Neural Networks
title_short Fall Detection Using Multiple Bioradars and Convolutional Neural Networks
title_sort fall detection using multiple bioradars and convolutional neural networks
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6960824/
https://www.ncbi.nlm.nih.gov/pubmed/31861061
http://dx.doi.org/10.3390/s19245569
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