Intrinsic Motivational States Can Be Classified by Non-Contact Measurement of Autonomic Nervous System Activation and Facial Expressions

Abstract

Motivation is a primary driver of goal-directed behavior. Therefore, the development of cost-effective and easily applicable systems to objectively quantify motivational states is needed. To achieve our goal, this study investigated the feasibility of classifying high- and low-motivation states by machine learning based on a diversity of features obtained by non-contact measurement of physiological responses and facial expression analysis. A random forest classifier with feature selection yielded modest success in the classification of high- and low-motivation states. Further analysis linked high-motivation states to the indices of autonomic nervous system activation reflective of reduced sympathetic activation and stronger, more intense expressions of happiness. The performance of motivational state classification systems should be further improved by incorporating different varieties of non-contact measurements.