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dc.contributor.authorCasilari-Pérez, Eduardo 
dc.contributor.authorLora Rivera, Raúl
dc.contributor.authorGarcía-Lagos, Francisco 
dc.date.accessioned2019-03-04T07:34:14Z
dc.date.available2019-03-04T07:34:14Z
dc.date.issued2019-03-04
dc.identifier.urihttps://hdl.handle.net/10630/17400
dc.descriptionArtículo sobre detección de caídas con redes neuronales profundasen_US
dc.description.abstractOwing to the effects of falls on quality of life of the elderly, automatic fall detection systems (FDS) have become a key research topic in the ambit of telecare. This works assesses the performance of convolutional neural networks when they are applied to identify fall accidents in a wearable FDS provided with a tri-axial accelerometer. The evaluation of the detection algorithm is carried out by employing a benchmarking repository with a wide set of traces captured from a wide group of volunteers that executed a programmed series of Activities of the Daily Living (ADLs) and emulated falls. Results show that the CNN can properly distinguish both types of movements with a success rate (specificity and sensitivity) around 99%.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.subjectRedes Neuronales (Informática)en_US
dc.subject.otherFall detection systemsen_US
dc.subject.otherWearableen_US
dc.subject.otherAccelerometersen_US
dc.titleEvaluation of a Fall Alerting System based on a Convolutional Deep Neural Networken_US
dc.typeinfo:eu-repo/semantics/conferenceObjecten_US
dc.centroE.T.S.I. Telecomunicaciónen_US
dc.relation.eventtitle6th International Conference on Control, Decision and Information Technologies (CODIT 2019)en_US
dc.relation.eventplaceParís, Franciaen_US
dc.relation.eventdate23 de abril de 2019en_US


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