Affective State Estimation Based On Russell’s Model And Physiological Measurements

This paper presents a reliable and efficient approach for real-time affective state estimation. The optimal physiological feature set and the most effective machine learning algorithm were identified and implemented. ReliefF feature selection algorithm was used to reduce the number of features from 23 to 13. Supervised learning algorithms such as K-Nearest Neighbors (KNN), cubic and gaussian Support Vector Machine, and Linear Discriminant Analysis were used to compare their effectiveness in affective state estimation. The results of the assessment of arousal and valence states on 20 participants indicate that KNN classifier, adopted with the 13 identified optimal features, is the most effective approach for real-time affective state estimation.