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dc.contributor.authorDelgado-Escaño, Rubén
dc.contributor.authorCastro, Francisco M.
dc.contributor.authorMarín-Jiménez, Manuel J.
dc.contributor.authorGuil-Mata, Nicolas 
dc.date.accessioned2019-11-26T09:00:50Z
dc.date.available2019-11-26T09:00:50Z
dc.date.created2019
dc.date.issued2019-11-26
dc.identifier.urihttps://hdl.handle.net/10630/18911
dc.description.abstractIn contrast to visual information that is recorded by cameras placed somewhere, inertial information can be obtained from mobile phones that are commonly used in daily life. We present in this talk a general deep learning approach for gait and soft biometrics (age and gender) recognition. Moreover, we also study the use of gait information to detect actions during walking, specifically, fall detection. We perform a thorough experimental evaluation of the proposed approach on different datasets: OU-ISIR Biometric Database, DFNAPAS, SisFall, UniMiB-SHAR and ASLH. The experimental results show that inertial information can be used for gait recognition and fall detection with state-of-the-art results.en_US
dc.description.sponsorshipUniversidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.en_US
dc.language.isoengen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectReconocimiento óptico de formas (Informática)en_US
dc.subject.otherGait recognitionen_US
dc.subject.otherIntertial sensorsen_US
dc.subject.otherConvolutional Neuronal Networksen_US
dc.subject.otherLong Short-term Memoryen_US
dc.titleGait recognition and fall detection with inertial sensorsen_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroE.T.S.I. Informáticaen_US
dc.relation.eventtitle2019 International Workshop on Human Identification at a Distanceen_US
dc.relation.eventplaceShenzhen, Chinaen_US
dc.relation.eventdate22/11/2019en_US
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*


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