An Extensible Modular Recognition Concept That Makes Activity Recognition Practical

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 un...

• Type: Journal
• Year: 2010
• Where: KI
• Authors: Martin Berchtold, Matthias Budde, Hedda Rahel Schmidtke, Michael Beigl
• Keywords: Activity Recognition Systems, Artificial Intelligence, KI 2010, Off-line Learning Methods, Uncertainty Value