A user-independent real-time emotion recognition system for software agents in domestic environments

The mystery surrounding emotions, how they work and how they affect our lives has not yet been unravelled. Scientists still debate the real nature of emotions, whether they are evolutionary, physiological or cognitive are just a few of the different approaches used to explain affective states. Regardless of the various emotional paradigms, neurologists have made progress in demonstrating that emotion is as, or more, important than reason in the process of making decisions and deciding actions. The significance of these findings should not be overlooked in a world that is increasingly reliant on computers to accommodate to user needs. In this paper, a novel approach for recognizing and classifying positive and negative emotional changes in real time using physiological signals is presented. Based on sequential analysis and autoassociative networks, the emotion detection system outlined here is potentially capable of operating on any individual regardless of their physical state and emotional intensity without requiring an arduous adaptation or pre-analysis phase. Results from applying this methodology on real-time data collected from a single subject demonstrated a recognition level of 71.4% which is comparable to the best results achieved by others through off-line analysis. It is suggested that the detection mechanism outlined in this paper has all the characteristics needed to perform emotion recognition in pervasive computing.