A Distributed Hierarchical Structure for Object Networks Supporting Human Activity Recognition

  • Venet Osmani
  • Sasitharan Balasubramaniam
  • Tao Gu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4267)


Pervasive environments will witness heterogeneous smart embedded devices (e.g. sensors, actuators) integrated into user’s living environment (e.g. smart homes and hospitals) and provide a multitude of information that can transparently support user’s lifestyle. One promising application resulting from the management and exploitation of this information is the human activity recognition. In this paper we briefly describe our activity recognition architecture and focus on an important management component of this architecture using the concept of object networks. We explore how object networks can integrate various sensor networks and heterogeneous devices into a coherent network through embedded context and role profile and at the same time support distributed context reasoning. The paper also describes the mechanisms used to eliminate and refine context information that is deemed irrelevant due to user behaviour changes over time, by employing the idea of role fitness.


Context Information Activity Recognition Smart Home Human Activity Recognition Role Interaction 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Philipose, M., Fishkin, K.P., Perkowitz, M., Patterson, D.J., Fox, D., Kautz, H., Hähnel, D.: Inferring Activities from Interactions with Objects. IEEE Pervasive Computing 03(4), 50–57 (2004)CrossRefGoogle Scholar
  2. 2.
    Bardram, J., Christensen, H.B.: Open Issues in Activity-Based and Task-Level Computing. In: First International Workshop on Computer Support for Human Tasks and Activities, CfPC PB-2004-60 (2004)Google Scholar
  3. 3.
    Guralnik, V., Haigh, K.Z.: Learning Models of Human Behaviour with Sequential Patterns. In: Proceedings of the AAAI 2002 workshop Automation as Caregiver, pp. 24–30 (2002)Google Scholar
  4. 4.
    Osmani, V., Balasubramaniam, S.: Context Management Support for Activity Recognition in Health-Care. In: Strang, T., Cahill, V., Quigley, A. (eds.) Pervasive 2006 Workshop Proceedings, 3rd International Workshop on Tangible Space Intiative, pp. 453–465 (2006) ISBN 978-3-00-018411-6Google Scholar
  5. 5.
    Dey, A.K.: Providing Architectural Support for Building Context-Aware Applications, PhD Thesis, Georgia Institute of Technology (2000)Google Scholar
  6. 6.
    Chen, H., Finin, T., Joshi, A.: An Intelligent Broker for Context-Aware Systems. In: Dey, A.K., Schmidt, A., McCarthy, J.F. (eds.) UbiComp 2003. LNCS, vol. 2864. Springer, Heidelberg (2003)Google Scholar
  7. 7.
    Gu, T., Wang, X.H., Pung, H.K., Zhang, D.Q.: An Ontology-based Context Model in Intelligent Environments. In: Proceedings of Communication Networks and Distributed Systems Modeling and Simulation Conference, pp. 270–275 (2004)Google Scholar
  8. 8.
    Newmad Technologies, “Newmad Technologies AB” (accessed, 04/08/2006) (2006), Available from:
  9. 9.
    Nakano, T., Suda, T.: Self-organizing network services with evolutionary adaptation. IEEE Trans. Neural Netw. 16(5), 1269–1278 (2005)CrossRefGoogle Scholar

Copyright information

© IFIP International Federation for Information Processing 2006

Authors and Affiliations

  • Venet Osmani
    • 1
  • Sasitharan Balasubramaniam
    • 1
  • Tao Gu
    • 2
  1. 1.Telecommunications Software and Systems GroupWaterford Institute of TechnologyIreland
  2. 2.Institute for Infocomm ResearchSingapore

Personalised recommendations