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dc.contributor.authorGómez Ramos, Raúl
dc.contributor.authorDuque Domingo, Jaime
dc.contributor.authorZalama Casanova, Eduardo 
dc.contributor.authorGómez García-Bermejo, Jaime 
dc.date.accessioned2021-09-02T08:23:25Z
dc.date.available2021-09-02T08:23:25Z
dc.date.issued2021
dc.identifier.citationSensors, 2021, vol. 21, n. 16, 5270es
dc.identifier.issn1424-8220es
dc.identifier.urihttps://uvadoc.uva.es/handle/10324/48475
dc.descriptionProducción Científicaes
dc.description.abstractIn recent years, Artificial Intelligence Technologies (AIT) have been developed to improve the quality of life of the elderly and their safety in the home. This work focuses on developing a system capable of recognising the most usual activities in the daily life of an elderly person in real-time to enable a specialist to monitor the habits of this person, such as taking medication or eating the correct meals of the day. To this end, a prediction model has been developed based on recurrent neural networks, specifically on bidirectional LSTM networks, to obtain in real-time the activity being carried out by the individuals in their homes, based on the information provided by a set of different sensors installed at each person’s home. The prediction model developed in this paper provides a 95.42% accuracy rate, improving the results of similar models currently in use. In order to obtain a reliable model with a high accuracy rate, a series of processing and filtering processes have been carried out on the data, such as a method based on a sliding window or a stacking and re-ordering algorithm, that are subsequently used to train the neural network, obtained from the public database CASAS.es
dc.format.mimetypeapplication/pdfes
dc.language.isoenges
dc.publisherMDPIes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subject.classificationRecurrent neural networkses
dc.subject.classificationRedes neuronales recurrenteses
dc.subject.classificationNon-intrusive sensorses
dc.subject.classificationSensores no intrusivoses
dc.subject.classificationSmart homeses
dc.subject.classificationHogares inteligenteses
dc.titleDaily human activity recognition using non-intrusive sensorses
dc.typeinfo:eu-repo/semantics/articlees
dc.rights.holder© 2021 The Authorses
dc.identifier.doi10.3390/s21165270es
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/21/16/5270es
dc.peerreviewedSIes
dc.description.projectMinisterio de Ciencia, Innovación y Universidades (project RTI2018-096652-B-I00)es
dc.description.projectJunta de Castilla y León - Fondo Europeo de Desarrollo Regional (project VA233P18)es
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersiones
dc.subject.unesco1203.04 Inteligencia Artificiales


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