Training-Free Fuzzy Logic Based Human Activity Recognition
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초록
The accuracy of training-based activity recognition depends on the trainingprocedure and the extent to which the training dataset comprehensively represents theactivity and its varieties. Additionally, training incurs substantial cost and effort in theprocess of collecting training data. To address these limitations, we have developed atraining-free activity recognition approach based on a fuzzy logic algorithm that utilizesa generic activity model and an associated activity semantic knowledge. The approachis validated through experimentation with real activity datasets. Results show that thefuzzy logic based algorithms exhibit comparable or better accuracy than other trainingbased approaches.
Abstract
The accuracy of training-based activity recognition depends on the trainingprocedure and the extent to which the training dataset comprehensively represents theactivity and its varieties. Additionally, training incurs substantial cost and effort in theprocess of collecting training data. To address these limitations, we have developed atraining-free activity recognition approach based on a fuzzy logic algorithm that utilizesa generic activity model and an associated activity semantic knowledge. The approachis validated through experimentation with real activity datasets. Results show that thefuzzy logic based algorithms exhibit comparable or better accuracy than other trainingbased approaches.
참고문헌 (23)
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