Journal of Control Theory and Applications

, Volume 9, Issue 1, pp 18–27

Comparison of fusion methods based on DST and DBN in human activity recognition

  • Andrei Tolstikov
  • Xin Hong
  • Jit Biswas
  • Chris Nugent
  • Liming Chen
  • Guido Parente
Article

Abstract

Ambient assistive living environments require sophisticated information fusion and reasoning techniques to accurately identify activities of a person under care. In this paper, we explain, compare and discuss the application of two powerful fusion methods, namely dynamic Bayesian networks (DBN) and Dempster-Shafer theory (DST), for human activity recognition. Both methods are described, the implementation of activity recognition based on these methods is explained, and model acquisition and composition are suggested. We also provide functional comparison of both methods as well as performance comparison based on the publicly available activity dataset. Our findings show that in performance and applicability, both DST and DBN are very similar; however, significant differences exist in the ways the models are obtained. DST being top-down and knowledge-based, differs significantly in qualitative terms, when compared with DBN, which is data-driven. These qualitative differences between DST and DBN should therefore dictate the selection of the appropriate model to use, given a particular activity recognition application.

Keywords

Dynamic Bayesian networks Dempster-Shafer theory Healthcare monitoring Ambient assisted living Activity recognition 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. [1]
    J. Biswas, K. Sim, W. Huang, et al. Sensor based microcontext for mild dementia assistance[C]//Proceedings of the 3rd International Conference on Pervasive Technologies Related to Assistive Environments (PETRA). Samos, Greece, 2010: DOI 10.1145/1839294.1839318.Google Scholar
  2. [2]
    E. Kim, S. Helal, D. Cook. Human activity recognition and pattern discovery[J]. IEEE Pervasive Computing, 2010, 9(1): 48–53.CrossRefGoogle Scholar
  3. [3]
    D. Lymberopoulos, T. Teixeira, A. Savvides. Macroscopic human behavior interpretation using distributed imager and other sensors[J]. Proceedings of the IEEE, 2008, 96(10): 1657–1677.CrossRefGoogle Scholar
  4. [4]
    K. P. Murphy. Dynamic Bayesian Networks: Representation, Inference and Learning[D]. Berkeley: University of California, 2002.Google Scholar
  5. [5]
    G. Shafer. A Mathematical Theory of Evidence[M]. Princeton: Princeton University Press, 1976.Google Scholar
  6. [6]
    A. Tolstikov. Application-level Quality of Service and Information Quality Provisioning in Sensor Networks[D]. Singapore: National University of Singapore, 2010.Google Scholar
  7. [7]
    X. Hong, C. Nugent, M. Mulvenna, et al. Evidential fusion of sensor data for activity recognition in smart homes[J]. Pervasive and Mobile Computing, 2009, 5(3): 236–252.CrossRefGoogle Scholar
  8. [8]
    Z. Ghahramani. Learning dynamic bayesian networks[M]. Adaptive Processing of Sequences and Data Structures. Berlin: Springer-Verlag, 1997: 168–197.Google Scholar
  9. [9]
    A. P. Dempster, N. M. Laird, D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm[J]. Journal of the Royal Statistical Society: Series B, 1977, 39(1): 1–8.MathSciNetMATHGoogle Scholar
  10. [10]
    M. Perkowitz, M. Philipose, D. J. Patterson. Mining models of human activities from the web[C]//Proceedings of the 13th International World Wide Web Conference (www’ 04). New York: ACM Press, 2004: 573–582.CrossRefGoogle Scholar
  11. [11]
    T. L. M. van Kasteren, A. K. Noulas, G. Englebienne, et al. Accurate activity recognition in a home setting[C]//Proceedings of the 10th International Conference on Ubiquitous Computing (Ubicomp’ 08). New York: ACM Press, 2008: 1–8.CrossRefGoogle Scholar

Copyright information

© South China University of Technology, Academy of Mathematics and Systems Science, Chinese Academy of Sciences and Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Andrei Tolstikov
    • 1
  • Xin Hong
    • 2
  • Jit Biswas
    • 1
  • Chris Nugent
    • 2
  • Liming Chen
    • 2
  • Guido Parente
    • 2
  1. 1.Networking Protocols DepartmentInstitute for Infocomm ResearchSingaporeSingapore
  2. 2.School of Computing and MathematicsUniversity of UlsterNewtownabbeyUK

Personalised recommendations