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.citation | Álvarez de la Concepción, M.Á., Soria Morillo, L.M., Álvarez García, J.A. y González Abril, L. (2016). Mobile activity recognition and fall detection system for elderly people using Ameva algorithm. Pervasive and Mobile Computing, 34 (january 2017), 3-13. | |
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. | |
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 |
dcterms.identifier | https://ror.org/03yxnpp24 | |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/embargoedAccess | 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 |