Abstract
Emotions play a significant and powerful role in everyday life of human beings. Developing algorithms for computers to recognize an emotional expression is widely studied area. In this study, emotion recognition from Galvanic Skin Response signals was performed using time domain, wavelet and Empirical Mode Decomposition based features. Valence and arousal have been categorized and relationship between physiological signals and arousal and valence has been studied using k-Nearest Neighbors, Decision Tree, Random Forest and Support Vector Machine algorithms. We have achieved 81.81% and 89.29% accuracy rate for arousal and valence respectively.
Original language | English |
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Pages (from-to) | 3129-3136 |
Number of pages | 8 |
Journal | Istanbul University - Journal of Electrical and Electronics Engineering |
Volume | 17 |
Publication status | Published - 2017 |
Keywords
- Biomedical Signal Processing
- Decision Tree
- Emotion Recognition
- Galvanic Skin Response
- K-Nearest Neighbors
- Machine Learning
- Pattern Recognition
- Physiological Signal
- Random Forest
- Support Vector Machine