Improving Activity Recognition in Smart Environments with Ontological Modeling

  • Zachary WemlingerEmail author
  • Lawrence Holder
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8456)


The problem of activity recognition in smart environments has produced multiple divergent paths of research in an attempt to improve the usability and usefulness of smart environments. In this paper we merge these research paths by defining a method for mapping smart environment sensor activities into an ontologically defined semantic feature space. We show that by using this approach we are able to improve activity recognition by between 5–20 %.


Activity recognition Ontological modeling Ontologies Semantic Web 



Thanks to the CASAS project at Washington State University for making the data used in this study available. This work is supported in part by National Science Foundation grant DGE-0900781.


  1. 1.
    Bonino, D., Corno, F.: DogOnt - ontology modeling for intelligent domotic environments. In: Sheth, A.P., Staab, S., Dean, M., Paolucci, M., Maynard, D., Finin, T., Thirunarayan, K. (eds.) ISWC 2008. LNCS, vol. 5318, pp. 790–803. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  2. 2.
    Chen, L., Nugent, C.: Ontology-based activity recognition in intelligent pervasive environments. Int. J. Web Inf. Syst. 5(4), 410–430 (2009)CrossRefGoogle Scholar
  3. 3.
    Cook, D.: Learning setting-generalized activity models for smart spaces. IEEE Intell. Syst. 27(1), 32–38 (2010)CrossRefGoogle Scholar
  4. 4.
    Cook, D., Das, S.: Smart Environments: Technology, Protocols and Applications, vol. 43. Wiley, New York (2004)CrossRefGoogle Scholar
  5. 5.
    Cook, D., Feuz, K.D., Krishnan, N.C.: Transfer learning for activity recognition: A survey. Knowl. Inf. Syst. 36(3), 537–556 (2013)CrossRefGoogle Scholar
  6. 6.
    Cook, D.J., Schmitter-Edgecombe, M., et al.: Assessing the quality of activities in a smart environment. Methods Inf. Med. 48(5), 480 (2009)CrossRefGoogle Scholar
  7. 7.
    Dernbach, S., Das, B., Krishnan, N.C., Thomas, B.L., Cook, D.J.: Simple and complex activity recognition through smart phones. In: 2012 8th International Conference on Intelligent Environments (IE), pp. 214–221. IEEE (2012)Google Scholar
  8. 8.
    Krishnan, N.C., Cook, D.J.: Activity recognition on streaming sensor data. Pervasive Mob. Comput. 10, 138–154 (2014)CrossRefGoogle Scholar
  9. 9.
    Lawton, M.P., Brody, E.M.: Assessment of older people: self-maintaining and instrumental activities of daily living. The Gerontologist 9(3), 179–186 (1969)CrossRefGoogle Scholar
  10. 10.
    Matuszek, C., Cabral, J., Witbrock, M.J., DeOliveira, J.: An introduction to the syntax and content of Cyc. In: AAAI Spring Symposium: Formalizing and Compiling Background Knowledge and Its Applications to Knowledge Representation and Question Answering, pp. 44–49. Citeseer (2006)Google Scholar
  11. 11.
    Rashidi, P., Cook, D.J., Holder, L.B., Schmitter-Edgecombe, M.: Discovering activities to recognize and track in a smart environment. IEEE Trans. Knowl. Data Eng. 23(4), 527–539 (2011)CrossRefGoogle Scholar
  12. 12.
    Sahaf, Y.: Comparing Sensor Modalities for Activity Recognition. Master’s thesis, Washington State University (2011)Google Scholar
  13. 13.
    Sculley, D.: Large scale learning to rank. In: NIPS 2009 Workshop on Advances in Ranking, pp. 1–6 (2009)Google Scholar
  14. 14.
    Szewcyzk, S., Minor, B., Swedlove, B., Cook, D.: Annotating smart environment sensor data for activity learning. Technol. Health Care 17(3), 161–169 (2009)Google Scholar
  15. 15.
    Wang, X.H., Gu, T., Zhang, D.Q., Pung, H.K.: An ontology-based context model in intelligent environments. In: Proceedings of Communication Networks and Distributed Systems Modeling and Simulation Conference, vol. 2004, pp. 270–275 (2004)Google Scholar
  16. 16.
    Wemlinger, Z., Holder, L.: The COSE ontology: bringing the semantic web to smart environments. In: Abdulrazak, B., Giroux, S., Bouchard, B., Pigot, H., Mokhtari, M. (eds.) ICOST 2011. LNCS, vol. 6719, pp. 205–209. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.School of Electrical Engineering and Computer ScienceWashington State UniversityPullmanUSA

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