Improving Activity Classification Using Ontologies to Expand Features in Smart Environments

  • Alberto SalgueroEmail author
  • Macarena Espinilla
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, 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.


Activity recognition Smart environments Ontology Data-driven approaches Knowledge-driven approaches 



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.


  1. 1.
    Chandrasekaran, B., Josephson, J., Benjamins, V.: What are ontologies, and why do we need them? IEEE Intell. Syst. Appl. 14(1), 20–26 (1999)CrossRefGoogle Scholar
  2. 2.
    Chen, L., Hoey, J., Nugent, C., Cook, D., Yu, Z.: Sensor-based activity recognition. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 42(6), 790–808 (2012)CrossRefGoogle Scholar
  3. 3.
    Chen, L., Nugent, C.: Ontology-based activity recognition in intelligent pervasive environments. Int. J. Web Inf. Syst. 5(4), 410–430 (2009)CrossRefGoogle Scholar
  4. 4.
    Chen, L., Nugent, C., Okeyo, G.: An ontology-based hybrid approach to activity modeling for smart homes. IEEE Trans. Hum. Mach. Syst. 44(1), 92–105 (2014)CrossRefGoogle Scholar
  5. 5.
    Chen, L., Nugent, C., Wang, H.: A knowledge-driven approach to activity recognition in smart homes. IEEE Trans. Knowl. Data Eng. 24(6), 961–974 (2012)CrossRefGoogle Scholar
  6. 6.
    Cook, D., Schmitter-Edgecombe, M., Crandall, A., Sanders, C., Thomas, B.: Collecting and disseminating smart home sensor data in the CASAS project. In: Proceedings of the CHI Workshop on Developing Shared Home Behavior Datasets to Advance HCI and Ubiquitous Computing Research (2009)Google Scholar
  7. 7.
    Horrocks, I., Patel-Schneider, P., Van Harmelen, F.: From SHIQ and RDF to OWL: the making of a web ontology language. Web Semant. 1(1), 7–26 (2003)CrossRefGoogle Scholar
  8. 8.
    Knijff, J., Frasincar, F., Hogenboom, F.: Domain taxonomy learning from text: the subsumption method versus hierarchical clustering. Data Knowl. Eng. 83, 54–69 (2013)CrossRefGoogle Scholar
  9. 9.
    Korhonen, I., Parkka, J., Van Gils, M.: Health monitoring in the home of the future. IEEE Eng. Med. Biol. Mag. 22(3), 66–73 (2003)CrossRefGoogle Scholar
  10. 10.
    Li, C., Lin, M., Yang, L., Ding, C.: Integrating the enriched feature with machine learning algorithms for human movement and fall detection. J. Supercomput. 67(3), 854–865 (2014)CrossRefGoogle Scholar
  11. 11.
    Nugent, C., Synnott, J., Santanna, A., Espinilla, M., Cleland, I., Banos, O., Lundström, J., Hallberg, J., Calzada, A.: An initiative for the creation of open datasets within the pervasive healthcare, pp. 180–183 (2016)Google Scholar
  12. 12.
    Rafferty, J., Chen, L., Nugent, C., Liu, J.: Goal lifecycles and ontological models for intention based assistive living within smart environments. Comput. Syst. Sci. Eng. 30(1), 7–18 (2015)Google Scholar
  13. 13.
    Sirin, E., Parsia, B., Grau, B., Kalyanpur, A., Katz, Y.: Pellet: a practical OWL-DL reasoner. Web Semant. 5(2), 51–53 (2007)CrossRefGoogle Scholar
  14. 14.
    Uschold, M., Gruninger, M.: Ontologies: principles, methods and applications. Knowl. Eng. Rev. 11(2), 93–136 (1996)CrossRefGoogle Scholar
  15. 15.
    Van Kasteren, T., Noulas, A., Englebienne, G., Kröse, B.: Accurate activity recognition in a home setting, pp. 1–9 (2008)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Universidad de CádizCádizSpain
  2. 2.Universidad de JaénJaénSpain

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