Implementation of physiological signal based emotion recognition algorithm

Abstract

Emotion recognition plays an important role in depression detection. The proposed system aims at classifying emotions automatically by pre-processing the physiological signals, feature extraction followed by classification and analyzing classification accuracy. Support Vector Machine (SVM) has been used for classification because of its high recognition rates and superior performance compared to Bayesian and Regression-based classifiers. The data corresponding to eight emotion available in databases DEAP, MAHNOB-HCI has been used for training and testing the proposed system. The physiological signals namely Electromyogram (EMG), Blood Volume Pressure (BVP) and Galvanic Skin Response (GSR) from emotion Sentics database are considered. Classification accuracy of 75% has been obtained for five target emotions, namely, Joy, Grief, Anger, Hate and Reverence. An improved recognition rate of 91% has been obtained by using k-fold leave out one cross-validation to reduce the observation dependence of prediction when there is limited training data.