Emotion recognition from physiological signals using fusion of wavelet based features

In this paper we propose a new system for human emotion recognition based on multi resolution analysis of physiological signals. In our study we have used four kinds of bio signals EMG, RESP, ECG and SC recorded at the University of Augsburg. Daubechies Symlet, Haar and Morlet wavelet transform were applied to analyze the non-stationary signals. Physiological features was extracted from the most relevant wavelet coefficients and the feature vectors obtained from each signal were combined using multimodal fusion technique to construct one feature vector for each emotion. A support vector machine (SVM) was adopted as a pattern classifier, an improved recognition accuracy of 95% was obtained and it clearly proves the performance of our new wavelet based approach in emotion recognition.