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Mobile activity recognition and fall detection system for elderly people using Ameva algorithm

 

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dc.creator Álvarez de la Concepción, Miguel Ángel es
dc.creator Soria Morillo, Luis Miguel es
dc.creator Álvarez García, Juan Antonio es
dc.creator González Abril, Luis es
dc.date.accessioned 2017-02-27T11:21:36Z
dc.date.available 2017-02-27T11:21:36Z
dc.date.issued 2016
dc.identifier.issn 1574-1192 es
dc.identifier.uri http://hdl.handle.net/11441/54899
dc.description.abstract Currently, the lifestyle of elderly people is regularly monitored in order to establish guidelines for rehabilitation processes or ensure the welfare of this segment of the population. In this sense, activity recognition is essential to detect an objective set of behaviors throughout the day. This paper describes an accurate, comfortable and efficient system, which monitors the physical activity carried out by the user. An extension to an awarded activity recognition system that participated in the EvAAL 2012 and EvAAL 2013 competitions is presented. This approach uses data retrieved from accelerometer sensors to generate discrete variables and it is tested in a non-controlled environment. In order to achieve the goal, the core of the algorithm Ameva is used to develop an innovative selection, discretization and classification technique for activity recognition. Moreover, with the purpose of reducing the cost and increasing user acceptance and usability, the entire system uses only a smartphone to recover all the information required es
dc.description.sponsorship Ministerio de Economía y Competitividad HERMES TIN2013-46801-C4-1-r es
dc.description.sponsorship Junta de Andalucía Simon P11-TIC-8052 es
dc.description.sponsorship Junta de Andalucía M-Learning P11-TIC-7124 es
dc.format application/pdf es
dc.language.iso eng es
dc.publisher Elsevier es
dc.relation.ispartof Pervasive and Mobile Computing, 34 (january 2017), 3-13. es
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 Internacional *
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/ *
dc.subject Activity recognition es
dc.subject Artificial intelligent es
dc.subject Cognitive computing es
dc.subject Contextual information es
dc.subject Mobile environments es
dc.subject Smart-energy computing es
dc.title Mobile activity recognition and fall detection system for elderly people using Ameva algorithm es
dc.type info:eu-repo/semantics/article es
dc.type.version info:eu-repo/semantics/submittedVersion es
dc.rights.accessrights info:eu-repo/semantics/embargoAccess es
dc.contributor.affiliation Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos es
dc.contributor.affiliation Universidad de Sevilla. Departamento de Economía Aplicada I es
dc.relation.projectID info:eu-repo/grantAgreement/MINECO/TIN2013-46801-C4-1-r es
dc.relation.projectID Simon P11-TIC-8052 es
dc.relation.projectID M-Learning P11-TIC-7124 es
dc.date.embargoEndDate 2019-01
dc.relation.publisherversion http://www.sciencedirect.com/science/article/pii/S1574119216300505 es
dc.identifier.doi 10.1016/j.pmcj.2016.05.002 es
idus.format.extent 11 p. es
dc.journaltitle Pervasive and Mobile Computing es
dc.publication.volumen 34 es
dc.publication.issue january 2017 es
dc.publication.initialPage 3 es
dc.publication.endPage 13 es
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