Transfer learning and information retrieval applied to fall detection

Detecting falls in the elderly population is a very important issue that is related with the time of recovery. This study focuses on using wearable smart watches to monitor the movements of the user in order to detect patterns that might be related to fall events. The proposed solution explores Symb...

Descripción completa

Detalles Bibliográficos
Autores: Fáñez Kertelj, Mirko|||0009-0001-7430-1602, Villar Flecha, José Ramón|||0000-0001-6024-9527, Cal Marín, Enrique Antonio de la|||0000-0001-7142-7544, Sedano, Javier, González Suárez, Víctor Manuel|||0000-0002-0937-1882
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:inglés
OAI Identifier:oai:digibuo.uniovi.es:10651/56922
Acceso en línea:http://hdl.handle.net/10651/56922
https://dx.doi.org/10.1111/exsy.12522
Access Level:acceso abierto
Palabra clave:Fall detection
Machine Learning
Time Series
Transfer Learning
id ES_1b8e30a3a9aaec1a938cac5fc0e793c4
oai_identifier_str oai:digibuo.uniovi.es:10651/56922
network_acronym_str ES
network_name_str España
repository_id_str
spelling Transfer learning and information retrieval applied to fall detectionFáñez Kertelj, Mirko|||0009-0001-7430-1602Villar Flecha, José Ramón|||0000-0001-6024-9527Cal Marín, Enrique Antonio de la|||0000-0001-7142-7544Sedano, JavierGonzález Suárez, Víctor Manuel|||0000-0002-0937-1882Fall detectionMachine LearningTime SeriesTransfer LearningDetecting falls in the elderly population is a very important issue that is related with the time of recovery. This study focuses on using wearable smart watches to monitor the movements of the user in order to detect patterns that might be related to fall events. The proposed solution explores Symbolic Aggregate approXimation (SAX) Time Series representation, together with two information retrieval techniques enriched with transfer learning (TL). The solution is user centred; that is, a model is developed for each specific user. Basically, the fall detection approach makes use of a finite-state machine to detect peaks; the time series window embedding these peaks are represented using SAX. Assuming the data from the public fall detection data sets are valid, a dictionary is prepared using the most relevant words. This dic- tionary is then introduced as previous knowledge to an online learning classifier that is trained with normal activities of daily living. The two classifiers are evaluated and compared with two classical approaches. Before this comparison, two clustering approaches are studied to produce the bag of relevant words. A complete experimen- tation is included, which makes use of several publicly available data sets and also with a data set developed by the research group. Comparisons are performed for all the data sets, showing how the TL stage empowers the classifier. The results show that this solution produces high detection rates and at the same time performed simi- larly for all the individuals tested. Furthermore, the positive effects of TL in this con- text are clearly remarked.This research has been funded by the Spanish Ministry of Science and Innovation, under Project MINECO-TIN2017-84804-R, and by the Grant FCGRUPIN-IDI/2018/000226 project from the Asturias Regional Government (Gobierno del Principado de Asturias).20202020-01-01journal articlehttp://purl.org/coar/resource_type/c_6501AMhttp://purl.org/coar/version/c_ab4af688f83e57aainfo:eu-repo/semantics/articlehttp://hdl.handle.net/10651/56922https://dx.doi.org/10.1111/exsy.12522reponame:RUO. Repositorio Institucional de la Universidad de Oviedoinstname:Universidad de Oviedo (UNIOVI)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:digibuo.uniovi.es:10651/569222026-06-07T06:38:51Z
dc.title.none.fl_str_mv Transfer learning and information retrieval applied to fall detection
title Transfer learning and information retrieval applied to fall detection
spellingShingle Transfer learning and information retrieval applied to fall detection
Fáñez Kertelj, Mirko|||0009-0001-7430-1602
Fall detection
Machine Learning
Time Series
Transfer Learning
title_short Transfer learning and information retrieval applied to fall detection
title_full Transfer learning and information retrieval applied to fall detection
title_fullStr Transfer learning and information retrieval applied to fall detection
title_full_unstemmed Transfer learning and information retrieval applied to fall detection
title_sort Transfer learning and information retrieval applied to fall detection
dc.creator.none.fl_str_mv Fáñez Kertelj, Mirko|||0009-0001-7430-1602
Villar Flecha, José Ramón|||0000-0001-6024-9527
Cal Marín, Enrique Antonio de la|||0000-0001-7142-7544
Sedano, Javier
González Suárez, Víctor Manuel|||0000-0002-0937-1882
author Fáñez Kertelj, Mirko|||0009-0001-7430-1602
author_facet Fáñez Kertelj, Mirko|||0009-0001-7430-1602
Villar Flecha, José Ramón|||0000-0001-6024-9527
Cal Marín, Enrique Antonio de la|||0000-0001-7142-7544
Sedano, Javier
González Suárez, Víctor Manuel|||0000-0002-0937-1882
author_role author
author2 Villar Flecha, José Ramón|||0000-0001-6024-9527
Cal Marín, Enrique Antonio de la|||0000-0001-7142-7544
Sedano, Javier
González Suárez, Víctor Manuel|||0000-0002-0937-1882
author2_role author
author
author
author
dc.subject.none.fl_str_mv Fall detection
Machine Learning
Time Series
Transfer Learning
topic Fall detection
Machine Learning
Time Series
Transfer Learning
description Detecting falls in the elderly population is a very important issue that is related with the time of recovery. This study focuses on using wearable smart watches to monitor the movements of the user in order to detect patterns that might be related to fall events. The proposed solution explores Symbolic Aggregate approXimation (SAX) Time Series representation, together with two information retrieval techniques enriched with transfer learning (TL). The solution is user centred; that is, a model is developed for each specific user. Basically, the fall detection approach makes use of a finite-state machine to detect peaks; the time series window embedding these peaks are represented using SAX. Assuming the data from the public fall detection data sets are valid, a dictionary is prepared using the most relevant words. This dic- tionary is then introduced as previous knowledge to an online learning classifier that is trained with normal activities of daily living. The two classifiers are evaluated and compared with two classical approaches. Before this comparison, two clustering approaches are studied to produce the bag of relevant words. A complete experimen- tation is included, which makes use of several publicly available data sets and also with a data set developed by the research group. Comparisons are performed for all the data sets, showing how the TL stage empowers the classifier. The results show that this solution produces high detection rates and at the same time performed simi- larly for all the individuals tested. Furthermore, the positive effects of TL in this con- text are clearly remarked.
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/10651/56922
https://dx.doi.org/10.1111/exsy.12522
url http://hdl.handle.net/10651/56922
https://dx.doi.org/10.1111/exsy.12522
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
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
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
_version_ 1869404172628000768
score 15,301603

Similares en LA Referencia