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A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets
Due to the repercussion of falls on both the health and self-sufficiency of older people and on the financial sustainability of healthcare systems, the study of wearable fall detection systems (FDSs) has gained much attention during the last years. The core of a FDS is the algorithm that discriminat...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085732/ https://www.ncbi.nlm.nih.gov/pubmed/32155936 http://dx.doi.org/10.3390/s20051466 |
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author | Casilari, Eduardo Lora-Rivera, Raúl García-Lagos, Francisco |
author_facet | Casilari, Eduardo Lora-Rivera, Raúl García-Lagos, Francisco |
author_sort | Casilari, Eduardo |
collection | PubMed |
description | Due to the repercussion of falls on both the health and self-sufficiency of older people and on the financial sustainability of healthcare systems, the study of wearable fall detection systems (FDSs) has gained much attention during the last years. The core of a FDS is the algorithm that discriminates falls from conventional Activities of Daily Life (ADLs). This work presents and evaluates a convolutional deep neural network when it is applied to identify fall patterns based on the measurements collected by a transportable tri-axial accelerometer. In contrast with most works in the related literature, the evaluation is performed against a wide set of public data repositories containing the traces obtained from diverse groups of volunteers during the execution of ADLs and mimicked falls. Although the method can yield very good results when it is hyper-parameterized for a certain dataset, the global evaluation with the other repositories highlights the difficulty of extrapolating to other testbeds the network architecture that was configured and optimized for a particular dataset. |
format | Online Article Text |
id | pubmed-7085732 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-70857322020-03-25 A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets Casilari, Eduardo Lora-Rivera, Raúl García-Lagos, Francisco Sensors (Basel) Article Due to the repercussion of falls on both the health and self-sufficiency of older people and on the financial sustainability of healthcare systems, the study of wearable fall detection systems (FDSs) has gained much attention during the last years. The core of a FDS is the algorithm that discriminates falls from conventional Activities of Daily Life (ADLs). This work presents and evaluates a convolutional deep neural network when it is applied to identify fall patterns based on the measurements collected by a transportable tri-axial accelerometer. In contrast with most works in the related literature, the evaluation is performed against a wide set of public data repositories containing the traces obtained from diverse groups of volunteers during the execution of ADLs and mimicked falls. Although the method can yield very good results when it is hyper-parameterized for a certain dataset, the global evaluation with the other repositories highlights the difficulty of extrapolating to other testbeds the network architecture that was configured and optimized for a particular dataset. MDPI 2020-03-06 /pmc/articles/PMC7085732/ /pubmed/32155936 http://dx.doi.org/10.3390/s20051466 Text en © 2020 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 Casilari, Eduardo Lora-Rivera, Raúl García-Lagos, Francisco A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets |
title | A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets |
title_full | A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets |
title_fullStr | A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets |
title_full_unstemmed | A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets |
title_short | A Study on the Application of Convolutional Neural Networks to Fall Detection Evaluated with Multiple Public Datasets |
title_sort | study on the application of convolutional neural networks to fall detection evaluated with multiple public datasets |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7085732/ https://www.ncbi.nlm.nih.gov/pubmed/32155936 http://dx.doi.org/10.3390/s20051466 |
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