Increased Robustness in Context Detection and Reasoning Using Uncertainty Measures: Concept and Application

  • Martin Berchtold
  • Michael Beigl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5859)

Abstract

This paper reports on a novel recurrent fuzzy classification method for robust detection of context activities in an environment using either single or distributed sensors. It also introduces a classification of system architectures for uncertainty calculation in general. Our proposed novel method utilizes uncertainty measures for improvement of detection, fusion and aggregation of context knowledge. Uncertainty measurement calculations are based on our novel recurrent fuzzy system. We applied the method in a real application to recognize various applause (and non applause) situations, e.g. during a conference. Measurements were taken from mobile phone sensors (microphone, accel. if available) and acceleration sensory attached to a board marker. We show that we are able to improve robustness of detection using our novel recurrent fuzzy classifier in combination with uncertainty measures by ~30% on average. We also show that the use of multiple phones and distributed recognition in most cases allows to achieve a recognition rate between 90% and 100%.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Buchholz, T., Kuepper, A., Schiffers, M.: Quality of context: What it is and why we need it. In: Workshop of the HP OpenView University Association (2003)Google Scholar
  2. 2.
    Preuveneers, D., Berbers, Y.: Quality extensions and uncertainty handling for context ontologies. In: W. on Context and Ont. Theory, Practice and Appl. (2006)Google Scholar
  3. 3.
    Ranganathan, A., Al-Muhtadi, J., Campbell, R.H.: Reasoning about uncertain contexts in pervasive computing environments. IEEE Pervasive Computing (2004)Google Scholar
  4. 4.
    Truong, B.A., Lee, Y.K., Lee, S.Y.: Modeling uncertainty in context-aware computing. In: Computer and Information Science, ICIS (2005)Google Scholar
  5. 5.
    Berchtold, M., Decker, C., Riedel, T., Zimmer, T., Beigl, M.: Using a context quality measure for improving smart appliances. In: IWSAWC (2007)Google Scholar
  6. 6.
    Berchtold, M., Riedel, T., Beigl, M., Decker, C.: Awarepen - classfication probability and fuzziness in a context aware application. Ubiq. Intell. and Comp. (2008)Google Scholar
  7. 7.
    Gomni, V., Bersini, H.: Recurrent fuzzy systems. IEEE Fuzzy Systems (1994)Google Scholar
  8. 8.
    Chiu, S.: Method and software for extracting fuzzy classification rules by subtractive clustering. IEEE Control Systems Magazine, 461–465 (1996)Google Scholar
  9. 9.
    Tagaki, T., Sugeno, M.: Fuzzy identification of systems and its application to modelling and control. Syst., Man and Cybernetics (1985)Google Scholar
  10. 10.
    Weisbrod, J.: Unscharfes schliessen. Diss. zur Kuenstlichen Intelligenz (1996)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Martin Berchtold
    • 1
  • Michael Beigl
    • 1
  1. 1.Distributed and Ubiquitous SystemsTU BraunschweigBraunschweigGermany

Personalised recommendations