Affective Feedback in a Virtual Reality based Intelligent Supermarket

Abstract

The probabilistic nature of the inferences in a context-aware intelligent environment (CAIE) renders them vulnerable to erroneous decisions resulting in wrong services. Learning to recognize a user’s negative reactions to such wrong services will enable a CAIE to anticipate a service’s appropriateness. We propose a framework for continuous measurement of physiology to infer a user’s negative-emotions arising from receiving wrong services, thereby implementing an implicit-feedback loop in the CAIE system. To induce such negative-emotions, in this paper, we present a virtualreality (VR) based experimental platform while collecting real-time physiological data from ambulatory wearable sensors. Results from the electrodermal activity (EDA) data analysis reveal patterns that correlate with known features of negative-emotions, indicating the possibility to infer service appropriateness from user’s reactions to a service, thereby closing an implicit-feedback loop for the CAIE.