Reasoning about Uncertain Contexts in Pervasive Computing Environments
IEEE Pervasive Computing
Modeling Uncertainty in Context-Aware Computing
Proceedings of the Fourth Annual ACIS International Conference on Computer and Information Science
Using a Context Quality Measure for Improving Smart Appliances
ICDCSW '07 Proceedings of the 27th International Conference on Distributed Computing Systems Workshops
AwarePen - Classification Probability and Fuzziness in a Context Aware Application
UIC '08 Proceedings of the 5th international conference on Ubiquitous Intelligence and Computing
An extensible modular recognition concept that makes activity recognition practical
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
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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%.