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http://dx.doi.org/10.3745/JIPS.04.0005

Training-Free Fuzzy Logic Based Human Activity Recognition  

Kim, Eunju (Mobile and Pervasive Computing Lab., Department of Computer and Information Science and Engineering, University of Florida)
Helal, Sumi (Mobile and Pervasive Computing Lab., Department of Computer and Information Science and Engineering, University of Florida)
Publication Information
Journal of Information Processing Systems / v.10, no.3, 2014 , pp. 335-354 More about this Journal
Abstract
The accuracy of training-based activity recognition depends on the training procedure and the extent to which the training dataset comprehensively represents the activity and its varieties. Additionally, training incurs substantial cost and effort in the process of collecting training data. To address these limitations, we have developed a training-free activity recognition approach based on a fuzzy logic algorithm that utilizes a generic activity model and an associated activity semantic knowledge. The approach is validated through experimentation with real activity datasets. Results show that the fuzzy logic based algorithms exhibit comparable or better accuracy than other training-based approaches.
Keywords
Activity Semantic Knowledge; Fuzzy Logic; Human Activity Recognition; Multi-Layer Neural Network;
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