Training-Free Fuzzy Logic Based Human Activity Recognition


Eunju Kim, Sumi Helal, Journal of Information Processing Systems Vol. 10, No. 3, pp. 335-354, Jun. 2014  

10.3745/JIPS.04.0005
Keywords: Activity Semantic Knowledge, Fuzzy Logic, Human Activity Recognition, Multi-Layer Neural Network
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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 trainingbased approaches.


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Cite this article
[APA Style]
Eunju Kim and Sumi Helal (2014). Training-Free Fuzzy Logic Based Human Activity Recognition. Journal of Information Processing Systems, 10(3), 335-354. DOI: 10.3745/JIPS.04.0005.

[IEEE Style]
E. Kim and S. Helal, "Training-Free Fuzzy Logic Based Human Activity Recognition," Journal of Information Processing Systems, vol. 10, no. 3, pp. 335-354, 2014. DOI: 10.3745/JIPS.04.0005.

[ACM Style]
Eunju Kim and Sumi Helal. 2014. Training-Free Fuzzy Logic Based Human Activity Recognition. Journal of Information Processing Systems, 10, 3, (2014), 335-354. DOI: 10.3745/JIPS.04.0005.