Show simple item record

dc.contributor.authorSalguero Hidalgo, Alberto G.
dc.contributor.authorDelatorre, Pablo
dc.contributor.authorMedina, Javier
dc.contributor.authorEspinilla, Macarena
dc.contributor.authorTomeu Hardasmal, Antonio Jorge
dc.contributor.otherIngeniería Informáticaen_US
dc.date.accessioned2019-05-07T08:31:16Z
dc.date.available2019-05-07T08:31:16Z
dc.date.issued2019-04
dc.identifier.issn1058-9244
dc.identifier.urihttp://hdl.handle.net/10498/21258
dc.description.abstractThe miniaturization and price reduction of sensors have encouraged the proliferation of smart environments, in which multitudinous sensors detect and describe the activities carried out by inhabitants. In this context, the recognition of activities of daily living has represented one of the most developed research areas in recent years. Its objective is to determine what daily activity is developed by the inhabitants of a smart environment. In this field, many proposals have been presented in the literature, many of them being based on ad hoc ontologies to formalize logical rules, which hinders their reuse in other contexts. In this work, we propose the use of class expression learning (CEL), an ontology-based data mining technique, for the recognition of ADL. This technique is based on combining the entities in the ontology, trying to find the expressions that best describe those activities. As far as we know, it is the first time that this technique is applied to this problem. To evaluate the performance of CEL for the automatic recognition of activities, we have first developed a framework that is able to convert many of the available datasets to all the ontology models we have found in the literature for dealing with ADL. Two different CEL algorithms have been employed for the recognition of eighteen activities in two different datasets. Although all the available ontologies in the literature are focused on the description of the context of the activities, the results show that the sequence of the events produced by the sensors is more relevant for their automatic recognition, in general terms.en_US
dc.formatapplication/pdfen_US
dc.language.isoengen_US
dc.publisherHindawien_US
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.sourceScientific Programming Volume 2019, Article ID 2917294en_US
dc.titleOntology-Based Framework for the Automatic Recognition of Activities of Daily Living Using Class Expression Learning Techniquesen_US
dc.typeinfo:eu-repo/semantics/articleen_US
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen_US
dc.identifier.doi10.1155/2019/2917294


Files in this item

This item appears in the following Collection(s)

Show simple item record

Atribución 4.0 Internacional
This work is under a Creative Commons License Atribución 4.0 Internacional