Active Affective State Detection and User Assistance With Dynamic Bayesian Networks

With the rapid development of pervasive and ubiquitous computing applications, intelligent user-assistance systems face challenges of ambiguous, uncertain, and multimodal sensory observations, user's changing state, and various constraints on available resources and costs in making decisions. We introduce a new probabilistic framework based on the dynamic Bayesian networks (DBNs) to dynamically model and recognize user's affective states and to provide the appropriate assistance in order to keep user in a productive state. We incorporate an active sensing mechanism into the DBN framework to perform purposive and sufficing information integration in order to infer user's affective state and to provide correct assistance in a timely and efficient manner. Experiments involving both synthetic and real data demonstrate the feasibility of the proposed framework as well as the effectiveness of the proposed active sensing strategy.