Mostrar el registro sencillo del ítem

dc.contributor.authorGómez Ramos, Raúl
dc.contributor.authorDuque Domingo, Jaime
dc.contributor.authorZalama Casanova, Eduardo 
dc.contributor.authorGómez García-Bermejo, Jaime 
dc.date.accessioned2024-04-11T11:07:19Z
dc.date.available2024-04-11T11:07:19Z
dc.date.issued2023
dc.identifier.citationElectronics, 2023, Vol. 12, Nº. 23, 4772es
dc.identifier.issn2079-9292es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/67137
dc.descriptionProducción Científicaes
dc.description.abstractAs people get older, living at home can expose them to potentially dangerous situations when performing everyday actions or simple tasks due to physical, sensory or cognitive limitations. This could compromise the residents’ health, a risk that in many cases could be reduced by early detection of the incidents. The present work focuses on the development of a system capable of detecting in real time the main activities of daily life that one or several people can perform at the same time inside their home. The proposed approach corresponds to an unsupervised learning method, which has a number of advantages, such as facilitating future replication or improving control and knowledge of the internal workings of the system. The final objective of this system is to facilitate the implementation of this method in a larger number of homes. The system is able to analyse the events provided by a network of non-intrusive sensors and the locations of the residents inside the home through a Bluetooth beacon network. The method is built upon an accurate combination of two hidden Markov models: one providing the rooms in which the residents are located and the other providing the activity the residents are carrying out. The method has been tested with the data provided by the public database SDHAR-HOME, providing accuracy results ranging from 86.78% to 91.68%. The approach presents an improvement over existing unsupervised learning methods as it is replicable for multiple users at the same time.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherMDPIes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectHome automationes
dc.subjectDomóticaes
dc.subjectInternet of thingses
dc.subjectInternet de las cosases
dc.subjectInternet - Tecnologíaes
dc.subjectHuman activity recognitiones
dc.subjectHidden Markov modelses
dc.subjectVoz - Informáticaes
dc.subjectSensor networkses
dc.subjectComputer Communication Networkses
dc.titleAn unsupervised method to recognise human activity at home using non-intrusive sensorses
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2023 The authorses
dc.identifier.doi10.3390/electronics12234772es
dc.relation.publisherversionhttps://www.mdpi.com/2079-9292/12/23/4772es
dc.identifier.publicationfirstpage4772es
dc.identifier.publicationissue23es
dc.identifier.publicationtitleElectronicses
dc.identifier.publicationvolume12es
dc.peerreviewedSIes
dc.description.projectMinisterio de Ciencia e Innovación/Agencia Estatal de Investigación (AEI)/10.13039/501100011033 y Fondo Europeo de Desarrollo Regional (FEDER) - (Project ROSOGAR PID2021-123020OB-I00)es
dc.description.projectJunta de Castilla y León, Consejería de Familia y Unión Europea NextGenerationEU- (proyecto EIAROB)es
dc.identifier.essn2079-9292es
dc.rightsAtribución 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones
dc.subject.unesco1203.17 Informáticaes
dc.subject.unesco1203.25 Diseño de Sistemas Sensoreses


Ficheros en el ítem

Thumbnail

Este ítem aparece en la(s) siguiente(s) colección(ones)

Mostrar el registro sencillo del ítem