RT Journal Article
T1 Ontology-Based Framework for the Automatic Recognition of Activities of Daily Living Using Class Expression Learning Techniques
A1 Salguero-Hidalgo, Alberto Gabriel
A1 De la Torre-Moreno, Pablo
A1 Medina-Quero, Javier
A1 Espinilla-Estévez, Macarena
A1 Tomeu, Antonio
A2 Riguzzi, Fabrizio
K1 Sistemas expertos
AB The 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.
PB John Wiley & Sons Ltd
SN 1058-9244
YR 2019
FD 2019-04-01
LK https://hdl.handle.net/10630/35615
UL https://hdl.handle.net/10630/35615
LA eng
NO Salguero, Alberto G., Delatorre, Pablo, Medina, Javier, Espinilla, Macarena, Tomeu, Antonio J., Ontology-Based Framework for the Automatic Recognition of Activities of Daily Living Using Class Expression Learning Techniques, Scientific Programming, 2019, 2917294, 19 pages, 2019. https://doi.org/10.1155/2019/2917294
NO This work was supported by the REMIND project, which has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement no. 734355.
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 20 ene 2026