Knowledge-Driven Activity Recognition and Segmentation Using Context Connections

  • Georgios Meditskos
  • Efstratios Kontopoulos
  • Ioannis Kompatsiaris
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8797)

Abstract

We propose a knowledge-driven activity recognition and segmentation framework introducing the notion of context connections. Given an RDF dataset of primitive observations, our aim is to identify, link and classify meaningful contexts that signify the presence of complex activities, coupling background knowledge pertinent to generic contextual dependencies among activities. To this end, we use the Situation concept of the DOLCE+DnS Ultralite (DUL) ontology to formally capture the context of high-level activities. Moreover, we use context similarity measures to handle the intrinsic characteristics of pervasive environments in real-world conditions, such as missing information, temporal inaccuracies or activities that can be performed in several ways. We illustrate the performance of the proposed framework through its deployment in a hospital for monitoring activities of Alzheimer’s disease patients.

Keywords

ontologies activity recognition segmentation context 

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References

  1. 1.
    Anicic, D., Fodor, P., Rudolph, S., Stojanovic, N.: Ep-sparql: a unified language for event processing and stream reasoning. In: Proceedings of the 20th International Conference on World Wide Web, pp. 635–644. ACM (2011)Google Scholar
  2. 2.
    Baader, F., Calvanese, D., McGuinness, D.L., Nardi, D., Patel-Schneider, P.F. (eds.): The Description Logic Handbook: Theory, Implementation, and Applications. Cambridge University Press (2003)Google Scholar
  3. 3.
    Barbieri, D., Braga, D., Ceri, S., Della Valle, E., Grossniklaus, M.: C-SPARQL: A Continuous Query Language for RDF Data Streams. International Journal of Semantic Computing (IJSC) 4(1) (2010)Google Scholar
  4. 4.
    Bettini, C., Brdiczka, O., Henricksen, K., Indulska, J., Nicklas, D., Ranganathan, A., Riboni, D.: A survey of context modelling and reasoning techniques. Pervasive Mob. Comput. 6(2), 161–180 (2010)CrossRefGoogle Scholar
  5. 5.
    Bikakis, A., Antoniou, G., Hasapis, P.: Strategies for contextual reasoning with conflicts in ambient intelligence. Knowl. and Infor. Systems 27(1), 45–84 (2011)CrossRefGoogle Scholar
  6. 6.
    Bishop, B., Kiryakov, A., Ognyanoff, D., Peikov, I., Tashev, Z., Velkov, R.: OWLIM: A family of scalable semantic repositories. Sem. Web 2(1), 33–42 (2011)Google Scholar
  7. 7.
    Chen, L., Khalil, I.: Activity recognition: Approaches, practices and trends. In: Activity Recognition in Pervasive Intelligent Environments, vol. 4, pp. 1–31 (2011)Google Scholar
  8. 8.
    Chen, L., Nugent, C.D.: Ontology-based activity recognition in intelligent pervasive environments. Int. Journal of Web Information Systems 5(4), 410–430 (2009)CrossRefGoogle Scholar
  9. 9.
    Chen, L., Nugent, C.D., Wang, H.: A knowledge-driven approach to activity recognition in smart homes. IEEE Trans. Knowl. and Data Engin. 24(6), 961–974 (2012)CrossRefGoogle Scholar
  10. 10.
    Cook, D.J., Augusto, J.C., Jakkula, V.R.: Ambient intelligence: Technologies, applications, and opportunities. Perv. and Mobile Computing 5(4), 277–298 (2009)CrossRefGoogle Scholar
  11. 11.
  12. 12.
    Eiter, T., Ianni, G., Krennwallner, T., Polleres, A.: Rules and ontologies for the semantic web. In: Baroglio, C., Bonatti, P.A., Małuszyński, J., Marchiori, M., Polleres, A., Schaffert, S. (eds.) Reasoning Web. LNCS, vol. 5224, pp. 1–53. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  13. 13.
    Gangemi, A., Mika, P.: Understanding the semantic web through descriptions and situations. In: Meersman, R., Schmidt, D.C. (eds.) CoopIS/DOA/ODBASE 2003. LNCS, vol. 2888, pp. 689–706. Springer, Heidelberg (2003)Google Scholar
  14. 14.
    Hong, X., Nugent, C.D., Mulvenna, M.D., Martin, S., Devlin, S., Wallace, J.G.: Dynamic similarity-based activity detection and recognition within smart homes. Int. J. Pervasive Computing and Communications 8(3), 264–278 (2012)CrossRefGoogle Scholar
  15. 15.
    Jekjantuk, N., Gröner, G., Pan, J.Z.: Modelling and reasoning in metamodelling enabled ontologies. In: Bi, Y., Williams, M.-A. (eds.) KSEM 2010. LNCS (LNAI), vol. 6291, pp. 51–62. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Knublauch, H., Hendler, J.A., Idehen, K.: SPIN - overview and motivation. W3C member submission, World Wide Web Consortium (February 2011)Google Scholar
  17. 17.
    Meditskos, G., Dasiopoulou, S., Efstathiou, V., Kompatsiaris, I.: Sp-act: A hybrid framework for complex activity recognition combining owl and sparql rules. In: PerCom Workshops, pp. 25–30 (2013)Google Scholar
  18. 18.
    Moser, T., Roth, H., Rozsnyai, S., Mordinyi, R., Biffl, S.: Semantic event correlation using ontologies. In: Meersman, R., Dillon, T., Herrero, P. (eds.) OTM 2009, Part II. LNCS, vol. 5871, pp. 1087–1094. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  19. 19.
    Motik, B., Cuenca Grau, B., Sattler, U.: Structured objects in OWL: representation and reasoning. In: World Wide Web, pp. 555–564 (2008)Google Scholar
  20. 20.
    Okeyo, G., Chen, L., Hui, W., Sterritt, R.: A hybrid ontological and temporal approach for composite activity modelling. In: TrustCom, pp. 1763–1770 (2012)Google Scholar
  21. 21.
    Okeyo, G., Chen, L., Wang, H., Sterritt, R.: Dynamic sensor data segmentation for real-time knowledge-driven activity recognition. Pervasive and Mobile Computing 10(Pt. B), 155–172 (2014)CrossRefGoogle Scholar
  22. 22.
    Palmes, P., Pung, H.K., Gu, T., Xue, W., Chen, S.: Object relevance weight pattern mining for activity recognition and segmentation. Perv. Mob. Comput. 6(1), 43–57 (2010)CrossRefGoogle Scholar
  23. 23.
    Patkos, T., Chrysakis, I., Bikakis, A., Plexousakis, D., Antoniou, G.: A reasoning framework for ambient intelligence. In: Konstantopoulos, S., Perantonis, S., Karkaletsis, V., Spyropoulos, C.D., Vouros, G. (eds.) SETN 2010. LNCS (LNAI), vol. 6040, pp. 213–222. Springer, Heidelberg (2010)Google Scholar
  24. 24.
    Riboni, D., Pareschi, L., Radaelli, L., Bettini, C.: Is ontology-based activity recognition really effective? In: Perv. Comp. and Commun., pp. 427–431 (2011)Google Scholar
  25. 25.
    Riboni, D., Bettini, C.: COSAR: hybrid reasoning for context-aware activity recognition. Personal Ubiquitous Comput. 15(3), 271–289 (2011)CrossRefGoogle Scholar
  26. 26.
    Riboni, D., Bettini, C.: OWL 2 modeling and reasoning with complex human activities. Pervasive and Mobile Computing 7(3), 379–395 (2011)CrossRefGoogle Scholar
  27. 27.
    Roy, P., Giroux, S., Bouchard, B., Bouzouane, A., Phua, C., Tolstikov, A., Biswas, J.: A possibilistic approach for activity recognition in smart homes for cognitive assistance to alzheimers patients. In: Activity Recognition in Pervasive Intelligent Environments, Atlantis Ambient and Pervasive Intelligence, vol. 4, pp. 33–58 (2011)Google Scholar
  28. 28.
    Teymourian, K., Paschke, A.: Semantic rule-based complex event processing. In: Governatori, G., Hall, J., Paschke, A. (eds.) RuleML 2009. LNCS, vol. 5858, pp. 82–92. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  29. 29.
    Teymourian, K., Rohde, M., Paschke, A.: Fusion of background knowledge and streams of events. In: Proceedings of the 6th ACM International Conference on Distributed Event-Based Systems, New York, NY, USA, pp. 302–313 (2012)Google Scholar
  30. 30.
    Tiberghien, T., Mokhtari, M., Aloulou, H., Biswas, J.: Semantic reasoning in context-aware assistive environments to support ageing with dementia. In: Cudré-Mauroux, P., et al. (eds.) ISWC 2012, Part II. LNCS, vol. 7650, pp. 212–227. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  31. 31.
    Wessel, M., Luther, M., Wagner, M.: The difference a day makes - recognizing important events in daily context logs. In: C&O:RR (2007)Google Scholar
  32. 32.
    Ye, J., Stevenson, G.: Semantics-driven multi-user concurrent activity recognition. In: Augusto, J.C., Wichert, R., Collier, R., Keyson, D., Salah, A.A., Tan, A.-H. (eds.) AmI 2013. LNCS, vol. 8309, pp. 204–219. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  33. 33.
    Zhang, S., McCullagh, P., Nugent, C., Zheng, H.: An ontology-based context-aware approach for behaviour analysis. In: Activity Recognition in Pervasive Intelligent Environments, vol. 4, pp. 127–148. Atlantis Press (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Georgios Meditskos
    • 1
  • Efstratios Kontopoulos
    • 1
  • Ioannis Kompatsiaris
    • 1
  1. 1.Information Technologies InstituteCentre for Research & Technology - HellasThessalonikiGreece

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