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%.
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References
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)
Preuveneers, D., Berbers, Y.: Quality extensions and uncertainty handling for context ontologies. In: W. on Context and Ont. Theory, Practice and Appl. (2006)
Ranganathan, A., Al-Muhtadi, J., Campbell, R.H.: Reasoning about uncertain contexts in pervasive computing environments. IEEE Pervasive Computing (2004)
Truong, B.A., Lee, Y.K., Lee, S.Y.: Modeling uncertainty in context-aware computing. In: Computer and Information Science, ICIS (2005)
Berchtold, M., Decker, C., Riedel, T., Zimmer, T., Beigl, M.: Using a context quality measure for improving smart appliances. In: IWSAWC (2007)
Berchtold, M., Riedel, T., Beigl, M., Decker, C.: Awarepen - classfication probability and fuzziness in a context aware application. Ubiq. Intell. and Comp. (2008)
Gomni, V., Bersini, H.: Recurrent fuzzy systems. IEEE Fuzzy Systems (1994)
Chiu, S.: Method and software for extracting fuzzy classification rules by subtractive clustering. IEEE Control Systems Magazine, 461–465 (1996)
Tagaki, T., Sugeno, M.: Fuzzy identification of systems and its application to modelling and control. Syst., Man and Cybernetics (1985)
Weisbrod, J.: Unscharfes schliessen. Diss. zur Kuenstlichen Intelligenz (1996)
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© 2009 Springer-Verlag Berlin Heidelberg
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Berchtold, M., Beigl, M. (2009). Increased Robustness in Context Detection and Reasoning Using Uncertainty Measures: Concept and Application. In: Tscheligi, M., et al. Ambient Intelligence. AmI 2009. Lecture Notes in Computer Science, vol 5859. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-05408-2_30
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DOI: https://doi.org/10.1007/978-3-642-05408-2_30
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-05407-5
Online ISBN: 978-3-642-05408-2
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