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.