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dc.contributor.authorGarcía-González, Daniel
dc.contributor.authorFernández-Blanco, Enrique
dc.contributor.authorRivero, Daniel
dc.contributor.authorRodríguez Luaces, Miguel
dc.date.accessioned2023-11-15T16:23:30Z
dc.date.available2023-11-15T16:23:30Z
dc.date.issued2023
dc.identifier.urihttp://hdl.handle.net/2183/34246
dc.descriptionCursos e Congresos, C-155es_ES
dc.description.abstract[Abstract] Human activity recognition (HAR) has garnered significant scientific interest in recent years. The widespread use of smartphones enabled convenient and cost-effective data collection, eliminating the need for additional wearables. Given that, this paper introduces a novel HAR dataset in which participants had freedom in choosing smartphone orientation and placement during activities, ensuring data variability. It also includes contributions from diverse individuals, reflecting unique smartphone usage habits. Moreover, it comprises measurements from accelerometer, gyroscope, magnetometer, and GPS, corresponding to one of four activities: inactive, active, walking, or driving. Unlike other datasets, the collected data in this study were obtained from smartphones used in real-life scenarioses_ES
dc.description.sponsorshipThis work was funded by CITIC is funded by the Xunta de Galicia through the collaboration agreement between the Consellería de Cultura, Educación, Formación Profesional e Universidades and the Galician universities for the reinforcement of the research centres of the Galician University System (CIGUS), Xunta de Galicia/FEDER-UE (ConectaPeme, GEMA: IN852A 2018/14), MINECO-AEI/FEDER-UE (Flatcity: TIN2016-77158-C4-3-R) and Xunta de Galicia/FEDER-UE (AXUDAS PARA A CONSOLIDACION E ESTRUTURACION DE UNIDADES DE INVESTIGACION COMPETITIVAS.GRC: ED431C 2017/58 and ED431C 2018/49).
dc.description.sponsorshipXunta de Galicia; ED431C 2017/58
dc.description.sponsorshipXunta de Galicia; ED431C 2018/49
dc.language.isoenges_ES
dc.publisherUniversidade da Coruña, Servizo de Publicaciónses_ES
dc.relationinfo:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/TIN2016-77158-C4-3-R/ES/VELOCITY: PROCESADO EFICIENTE DE BIG DATA ESPAZO-TEMPORAL PARA FLATCITY
dc.relation.urihttps://doi.org/10.17979/spudc.000024.08
dc.rightsAttribution 4.0 International (CC BY 4.0)es_ES
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/deed.es*
dc.subjectDatos HARes_ES
dc.subjectSmartphoneses_ES
dc.subjectDispositivos móvileses_ES
dc.titleIntroducing a Human Activity Recognition Dataset Gathered on Real-Life Conditionses_ES
dc.typeinfo:eu-repo/semantics/conferenceObjectes_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.startPage47es_ES
UDC.endPage54es_ES
UDC.conferenceTitleVI Congreso Xove TIC: impulsando el talento científico. Octubre, 2023, A Coruñaes_ES


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