| Summary: | 碩士 === 國立政治大學 === 資訊科學學系 === 97 === A living smart environment should be able to provide thoughtful services by considering different states of emotions. The goal of our research is to develop an emotion recognition system which can detect the internal emotion states from external varieties of physiological data.
First we applied the dimensional analysis approach and adopted IAPS (International Affective Picture System) to manipulate psychological experiments. We collected physiological data and subjective ratings for arousal and valence from 20 subjects. We proposed an emotion recognition learning algorithm. It would extract each pattern of emotions from cross validation training and can further learn adaptively by feeding personalized testing data. We measured the learning trend of each subject. The recognition rate reveals incremental enhancement. Furthermore, we adopted a dimensional to discrete emotion transforming concept for validating the subjective rating. Compared to the experiment results of related works, our system outperforms both in dimensional and discrete analyses.
Most importantly, the system is implemented based on wireless physiological sensors for mobile usage. This system can reflect the image of emotion states in order to provide on-line smart services.
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