Online motion recognition using an accelerometer in a mobile device

This paper introduces a new method to implement a motion recognition process using a mobile phone fitted with an accelerometer. The data collected from the accelerometer are interpreted by means of astatistical study and machine learning algorithms in order to obtain a classification function. Then,...

Descripción completa

Detalles Bibliográficos
Autores: Fuentes, D., González Abril, Luis, Angulo, C., Ortega Ramírez, Juan Antonio
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2012
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/142848
Acceso en línea:https://hdl.handle.net/11441/142848
https://doi.org/10.1016/j.eswa.2011.08.098
Access Level:acceso abierto
Palabra clave:Features extraction
Pattern recognition
SVM
id ES_48c5de31df4edb2ca76b7017fdc4baff
oai_identifier_str oai:idus.us.es:11441/142848
network_acronym_str ES
network_name_str España
repository_id_str
spelling Online motion recognition using an accelerometer in a mobile deviceFuentes, D.González Abril, LuisAngulo, C.Ortega Ramírez, Juan AntonioFeatures extractionPattern recognitionSVMThis paper introduces a new method to implement a motion recognition process using a mobile phone fitted with an accelerometer. The data collected from the accelerometer are interpreted by means of astatistical study and machine learning algorithms in order to obtain a classification function. Then, that function is implemented in a mobile phone and online experiments are carried out. Experimental results show that this approach can be used to effectively recognize different human activities with a high-level accuracy.Ministerio de Ciencia e Innovación TIN2009–14378-C02–01ScienceDirectLenguajes y Sistemas InformáticosMinisterio de Ciencia e Innovación (MICIN). España2012info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/142848https://doi.org/10.1016/j.eswa.2011.08.098reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésExpert Systems with Applications, 39 (3), 2461-2465.TIN2009–14378-C02–01https://www.sciencedirect.com/science/article/pii/S0957417411012292info:eu-repo/semantics/openAccessoai:idus.us.es:11441/1428482026-06-17T12:51:07Z
dc.title.none.fl_str_mv Online motion recognition using an accelerometer in a mobile device
title Online motion recognition using an accelerometer in a mobile device
spellingShingle Online motion recognition using an accelerometer in a mobile device
Fuentes, D.
Features extraction
Pattern recognition
SVM
title_short Online motion recognition using an accelerometer in a mobile device
title_full Online motion recognition using an accelerometer in a mobile device
title_fullStr Online motion recognition using an accelerometer in a mobile device
title_full_unstemmed Online motion recognition using an accelerometer in a mobile device
title_sort Online motion recognition using an accelerometer in a mobile device
dc.creator.none.fl_str_mv Fuentes, D.
González Abril, Luis
Angulo, C.
Ortega Ramírez, Juan Antonio
author Fuentes, D.
author_facet Fuentes, D.
González Abril, Luis
Angulo, C.
Ortega Ramírez, Juan Antonio
author_role author
author2 González Abril, Luis
Angulo, C.
Ortega Ramírez, Juan Antonio
author2_role author
author
author
dc.contributor.none.fl_str_mv Lenguajes y Sistemas Informáticos
Ministerio de Ciencia e Innovación (MICIN). España
dc.subject.none.fl_str_mv Features extraction
Pattern recognition
SVM
topic Features extraction
Pattern recognition
SVM
description This paper introduces a new method to implement a motion recognition process using a mobile phone fitted with an accelerometer. The data collected from the accelerometer are interpreted by means of astatistical study and machine learning algorithms in order to obtain a classification function. Then, that function is implemented in a mobile phone and online experiments are carried out. Experimental results show that this approach can be used to effectively recognize different human activities with a high-level accuracy.
publishDate 2012
dc.date.none.fl_str_mv 2012
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/142848
https://doi.org/10.1016/j.eswa.2011.08.098
url https://hdl.handle.net/11441/142848
https://doi.org/10.1016/j.eswa.2011.08.098
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Expert Systems with Applications, 39 (3), 2461-2465.
TIN2009–14378-C02–01
https://www.sciencedirect.com/science/article/pii/S0957417411012292
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv ScienceDirect
publisher.none.fl_str_mv ScienceDirect
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869407381277900800
score 15.301629

Similares en LA Referencia