Autonomous on-wrist acceleration-based fall detection systems: unsolved challenges

Fall detection (FD) has been the focus of many research studies during the last years. Developing reliable FD systems is relevant, for instance, to pro- vide support to the elderly population in their everyday life. Besides, the generalization of the use of wearable devices (and more specifically, o...

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Autores: Villar Flecha, José Ramón|||0000-0001-6024-9527, Chira, Camelia, Cal Marín, Enrique Antonio de la|||0000-0001-7142-7544, González Suárez, Víctor Manuel|||0000-0002-0937-1882, Sedano, Javier, Khojasteh, Samad B.
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad de Oviedo (UNIOVI)
Repositorio:RUO. Repositorio Institucional de la Universidad de Oviedo
Idioma:español
OAI Identifier:oai:digibuo.uniovi.es:10651/56923
Acceso en línea:http://hdl.handle.net/10651/56923
https://dx.doi.org/10.1016/j.neucom.2019.12.147
Access Level:acceso abierto
Palabra clave:Fall detection
Machine Learning
Elderly population
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spelling Autonomous on-wrist acceleration-based fall detection systems: unsolved challengesVillar Flecha, José Ramón|||0000-0001-6024-9527Chira, CameliaCal Marín, Enrique Antonio de la|||0000-0001-7142-7544González Suárez, Víctor Manuel|||0000-0002-0937-1882Sedano, JavierKhojasteh, Samad B.Fall detectionMachine LearningElderly populationFall detection (FD) has been the focus of many research studies during the last years. Developing reliable FD systems is relevant, for instance, to pro- vide support to the elderly population in their everyday life. Besides, the generalization of the use of wearable devices (and more specifically, on-wrist devices) to measure the daily activity strongly suggests that in a short period of time, the elderly people will be making use of this type of devices. On-wrist devices can be used as the FD basic sensing unit; while the intelligent classi- fication can be obtained either autonomously (on the device) or requested to a remote service (via the paired smartphone or via web services). This study tries to analyze the current challenges in autonomous on-wrist wearable de- vices for producing a reliable and robust FD system. To do so, we analyze the related work; one of the possible solutions is implemented with several alternatives and evaluated with publicly available simulated falls data sets. The most remarkable findings in this research are that i) real fall data sets are needed, at least, a valid merging method to produce real fall like Time Series, ii) generalized solutions might not be enough and research is needed in models that learns from the user, iii) the need of tuning and fitting to the current user performance, iv) the amount of fall types suggests that hybrid and ensemble approaches might be interesting.This research has been funded by the Spanish Ministry of Science and Innovation, under projects MINECO-TIN2014-56967-R and MINECO-TIN2017- 84804-R, and by the Grant FCGRUPIN-IDI/2018/000226 project from the Asturias Regional Government.20202020-01-01journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articlehttp://hdl.handle.net/10651/56923https://dx.doi.org/10.1016/j.neucom.2019.12.147reponame:RUO. Repositorio Institucional de la Universidad de Oviedoinstname:Universidad de Oviedo (UNIOVI)Españolspaopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:digibuo.uniovi.es:10651/569232026-06-07T06:38:51Z
dc.title.none.fl_str_mv Autonomous on-wrist acceleration-based fall detection systems: unsolved challenges
title Autonomous on-wrist acceleration-based fall detection systems: unsolved challenges
spellingShingle Autonomous on-wrist acceleration-based fall detection systems: unsolved challenges
Villar Flecha, José Ramón|||0000-0001-6024-9527
Fall detection
Machine Learning
Elderly population
title_short Autonomous on-wrist acceleration-based fall detection systems: unsolved challenges
title_full Autonomous on-wrist acceleration-based fall detection systems: unsolved challenges
title_fullStr Autonomous on-wrist acceleration-based fall detection systems: unsolved challenges
title_full_unstemmed Autonomous on-wrist acceleration-based fall detection systems: unsolved challenges
title_sort Autonomous on-wrist acceleration-based fall detection systems: unsolved challenges
dc.creator.none.fl_str_mv Villar Flecha, José Ramón|||0000-0001-6024-9527
Chira, Camelia
Cal Marín, Enrique Antonio de la|||0000-0001-7142-7544
González Suárez, Víctor Manuel|||0000-0002-0937-1882
Sedano, Javier
Khojasteh, Samad B.
author Villar Flecha, José Ramón|||0000-0001-6024-9527
author_facet Villar Flecha, José Ramón|||0000-0001-6024-9527
Chira, Camelia
Cal Marín, Enrique Antonio de la|||0000-0001-7142-7544
González Suárez, Víctor Manuel|||0000-0002-0937-1882
Sedano, Javier
Khojasteh, Samad B.
author_role author
author2 Chira, Camelia
Cal Marín, Enrique Antonio de la|||0000-0001-7142-7544
González Suárez, Víctor Manuel|||0000-0002-0937-1882
Sedano, Javier
Khojasteh, Samad B.
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Fall detection
Machine Learning
Elderly population
topic Fall detection
Machine Learning
Elderly population
description Fall detection (FD) has been the focus of many research studies during the last years. Developing reliable FD systems is relevant, for instance, to pro- vide support to the elderly population in their everyday life. Besides, the generalization of the use of wearable devices (and more specifically, on-wrist devices) to measure the daily activity strongly suggests that in a short period of time, the elderly people will be making use of this type of devices. On-wrist devices can be used as the FD basic sensing unit; while the intelligent classi- fication can be obtained either autonomously (on the device) or requested to a remote service (via the paired smartphone or via web services). This study tries to analyze the current challenges in autonomous on-wrist wearable de- vices for producing a reliable and robust FD system. To do so, we analyze the related work; one of the possible solutions is implemented with several alternatives and evaluated with publicly available simulated falls data sets. The most remarkable findings in this research are that i) real fall data sets are needed, at least, a valid merging method to produce real fall like Time Series, ii) generalized solutions might not be enough and research is needed in models that learns from the user, iii) the need of tuning and fitting to the current user performance, iv) the amount of fall types suggests that hybrid and ensemble approaches might be interesting.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-01-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
AM
http://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10651/56923
https://dx.doi.org/10.1016/j.neucom.2019.12.147
url http://hdl.handle.net/10651/56923
https://dx.doi.org/10.1016/j.neucom.2019.12.147
dc.language.none.fl_str_mv Español
spa
language_invalid_str_mv Español
language spa
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
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eu_rights_str_mv openAccess
dc.source.none.fl_str_mv reponame:RUO. Repositorio Institucional de la Universidad de Oviedo
instname:Universidad de Oviedo (UNIOVI)
instname_str Universidad de Oviedo (UNIOVI)
reponame_str RUO. Repositorio Institucional de la Universidad de Oviedo
collection RUO. Repositorio Institucional de la Universidad de Oviedo
repository.name.fl_str_mv
repository.mail.fl_str_mv
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