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Improving Activity Classification Using Ontologies to Expand Features in Smart Environments

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Ubiquitous Computing and Ambient Intelligence (UCAmI 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10586))


Activity recognition is a promising field of research aiming to develop solutions within smart environments to provide relevant solutions on ambient assisted living, among others. The process of activity recognition aims to recognize the actions and goals of one or more person in a environment with a set of sensors are deployed, basing on the sensor data stream that capture a series of observations of actions and environmental conditions. This contributions presents the initial results from a new methodology that considers the use of ontologies to expand the set of feature vector, which is computed by using the sensor data stream, that is used in the process of activity recognition by data-driven approaches. The obtained results indicates that the use of extended feature vectors provided by the use of ontology offers a better accuracy regarding the original feature vectors used in the process of activity recognition with different data-driven approaches.

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  1. 1. (last checked on April 19, 2017).

  2. 2.

    For simplicity, only the activation of the sensors has been taken into account for the experiment in Sect. 4.2. However, it is also possible to consider the deactivations of the sensors by just enabling a flag in the application developed for loading the dataset in the ontology.

  3. 3.

    The Weka implementation of the C4.5 classifier is called J48.


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This project has received funding from the European Unions Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 734355 together the Spanish government by research project TIN2015-66524-P.

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Correspondence to Alberto Salguero .

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Salguero, A., Espinilla, M. (2017). Improving Activity Classification Using Ontologies to Expand Features in Smart Environments. In: Ochoa, S., Singh, P., Bravo, J. (eds) Ubiquitous Computing and Ambient Intelligence. UCAmI 2017. Lecture Notes in Computer Science(), vol 10586. Springer, Cham.

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  • Print ISBN: 978-3-319-67584-8

  • Online ISBN: 978-3-319-67585-5

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