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dc.contributor.authorCollado Villaverde, Armando 
dc.contributor.authorCobos Maestre, Mario 
dc.contributor.authorMuñoz Martínez, Pablo 
dc.contributor.authorFernández Barrero, David 
dc.date.accessioned2021-06-25T13:19:27Z
dc.date.available2021-06-25T13:19:27Z
dc.date.issued2020-11-03
dc.identifier.bibliographicCitationCollado Villaverde A., Cobos M., Muñoz P. & F. Barrero, D. 2020, "A simulator to support machine learning-based wearable fall detection systems", Electrónics, vol. 9, no. 11.
dc.identifier.issn2079-9292
dc.identifier.urihttp://hdl.handle.net/10017/48769
dc.description.abstractPeople’s life expectancy is increasing, resulting in a growing elderly population. That population is subject to dependency issues, falls being a problematic one due to the associated health complications. Some projects are trying to enhance the independence of elderly people by monitoring their status, typically by means of wearable devices. These devices often feature Machine Learning (ML) algorithms for fall detection using accelerometers. However, the software deployed often lacks reliable data for the models’ training. To overcome such an issue, we have developed a publicly available fall simulator capable of recreating accelerometer fall samples of two of the most common types of falls: syncope and forward. Those simulated samples are like real falls recorded using real accelerometers in order to use them later as input for ML applications. To validate our approach, we have used different classifiers over both simulated falls and data from two public datasets based on real data. Our tests show that the fall simulator achieves a high accuracy for generating accelerometer data from a fall, allowing to create larger datasets for training fall detection software in wearable devices.en
dc.description.sponsorshipJunta de Comunidades de Castilla-La Manchaes_ES
dc.description.sponsorshipComunidad de Madrides_ES
dc.description.sponsorshipAgencia Estatal de Investigaciónes_ES
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherMDPI
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectFall detectionen
dc.subjectMachine learningen
dc.subjectSimulationen
dc.subjectWearable devicesen
dc.titleA simulator to support machine learning-based wearable fall detection systemsen
dc.typeinfo:eu-repo/semantics/articleen
dc.subject.ecienciaInformáticaes_ES
dc.subject.ecienciaComputer scienceen
dc.contributor.affiliationUniversidad de Alcalá. Departamento de Automáticaes_ES
dc.contributor.affiliationUniversidad de Alcalá. Departamento de Ciencias de la Computaciónes_ES
dc.date.updated2021-06-25T13:18:21Z
dc.relation.publisherversionhttps://doi.org/10.3390/electronics9111831
dc.type.versioninfo:eu-repo/semantics/publishedVersionen
dc.identifier.doi10.3390/electronics9111831
dc.relation.projectIDinfo:eu-repo/grantAgreement/JCCM//SBPLY%2F19%2F180501%2F000024/ES/MEJORA DE LA GESTIÓN DE RECURSOS HOSPITALARIOS MEDIANTE LA PREDICCIÓN DE LA DEMANDA CON APRENDIZAJE AUTOMÁTICO Y PLANIFICACIÓNes_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-109891RB-I00/ES/MEJORA DE LA GESTION DE RECURSOS HOSPITALARIOS MEDIANTE LA PREDICCION DE LA DEMANDA CON APRENDIZAJE AUTOMATICO Y PLANIFICACION/es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.uxxiAR/0000035198
dc.identifier.publicationtitleElectronics
dc.identifier.publicationvolume9
dc.identifier.publicationissue11


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