An extensible modular recognition concept that makes activity recognition practical

Authors:
Martin Berchtold;Matthias Budde;Hedda R. Schmidtke;Michael Beigl
Affiliations:
Institute of Operating Systems and Computer Networks, TU Braunschweig;Institute of Telematics, Pervasive Computing Chair, Karlsruhe Institute of Technology;Institute of Telematics, Pervasive Computing Chair, Karlsruhe Institute of Technology;Institute of Telematics, Pervasive Computing Chair, Karlsruhe Institute of Technology
Venue:
KI'10 Proceedings of the 33rd annual German conference on Advances in artificial intelligence
Year:
2010

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Abstract

In mobile and ubiquitous computing, there is a strong need for supporting different users with different interests, needs, and demands. Activity recognition systems for context aware computing applications usually employ highly optimized off-line learning methods. In such systems, a new classifier can only be added if the whole recognition system is redesigned. For many applications that is not a practical approach. To be open for new users and applications, we propose an extensible recognition system with a modular structure. We will show that such an approach can produce almost the same accuracy compared to a system that has been generally trained (only 2 percentage points lower). Our modular classifier system allows the addition of new classifier modules. These modules use Recurrent Fuzzy Inference Systems (RFIS) as mapping functions, that not only deliver a classification, but also an uncertainty value describing the reliability of the classification. Based on the uncertainty value we are able to boost recognition rates. A genetic algorithm search enables the modular combination.