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Sensors 2018, 18(4), 1202; doi:10.3390/s18041202

Using Ontologies for the Online Recognition of Activities of Daily Living

1
Department of Computer Science, University of Cádiz, Cádiz 11519, Spain
2
Department of Computer Science, University of Jaén, Jaén 23071, Spain
This paper is an extended version of our paper published in Salguero, A.; Espinilla M. Improving activity classification using ontologies to expand features in smart environments. In Ubiquitous Computing and Ambient Intelligence (UCAmI 2017); Ochoa, S., Singh, P., Bravo, J., Eds.; Lecture Notes in Computer Science, Springer: Cham, Switzerland, 2017; Volume 10586; pp. 381–393. Best Paper Award.
*
Author to whom correspondence should be addressed.
Received: 15 March 2018 / Revised: 10 April 2018 / Accepted: 11 April 2018 / Published: 14 April 2018
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Abstract

The recognition of activities of daily living is an important research area of interest in recent years. The process of activity recognition aims to recognize the actions of one or more people in a smart environment, in which a set of sensors has been deployed. Usually, all the events produced during each activity are taken into account to develop the classification models. However, the instant in which an activity started is unknown in a real environment. Therefore, only the most recent events are usually used. In this paper, we use statistics to determine the most appropriate length of that interval for each type of activity. In addition, we use ontologies to automatically generate features that serve as the input for the supervised learning algorithms that produce the classification model. The features are formed by combining the entities in the ontology, such as concepts and properties. The results obtained show a significant increase in the accuracy of the classification models generated with respect to the classical approach, in which only the state of the sensors is taken into account. Moreover, the results obtained in a simulation of a real environment under an event-based segmentation also show an improvement in most activities. View Full-Text
Keywords: activity recognition; smart environments; ontology; data-driven approaches; knowledge-driven approaches activity recognition; smart environments; ontology; data-driven approaches; knowledge-driven approaches
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Salguero, A.G.; Espinilla, M.; Delatorre, P.; Medina, J. Using Ontologies for the Online Recognition of Activities of Daily Living. Sensors 2018, 18, 1202.

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