Seven Principles to Mine Flexible Behavior from Physiological Signals for Effective Emotion Recognition and Description in Affective Interactions

Abstract

Measuring affective interactions using physiological signals has become a critical step to understand engagements with human and artificial agents. However, traditional methods for signal analysis are not yet able to effectively deal with the differences of responses across individuals and with flexible sequential behavior. In this work, we rely on empirical results to define seven principles for a robust mining of physiological signals to recognize and characterize affective states. The majority of these principles are novel and driven from advanced pre-processing techniques and temporal data mining methods. A methodology that integrates these principles is proposed and validated using electrodermal signals collected during human-to-human and human-to-robot affective interactions.