An empirical study of machine learning techniques for affect recognition in human-robot interaction

Given the importance of implicit communication in human interactions, it would be valuable to have this capability in robotic systems wherein a robot can detect the motivations and emotions of the person it is working with. Recognizing affective states from physiological cues is an effective way of implementing implicit human-robot interaction. Several machine learning techniques have been successfully employed in affect-recognition to predict the affective state of an individual given a set of physiological features. However, a systematic comparison of the strengths and weaknesses of these methods has not yet been done. In this paper we present a comparative study of four machine learning methods - k-nearest neighbor, regression tree, Bayesian network and support vector machine as applied to the domain of affect recognition using physiological signals. The results showed that support vector machine gave the best classification accuracy even though all the methods performed competitively. Regression tree gave the next best classification accuracy and was the most space and time efficient.