An asynchronous Dynamic Bayesian Network for activity recognition in an Ambient Intelligent environment

Ambient Intelligence is the future of computing where devices predict what users need and help them carry out their everyday life activities easier. To make this prediction possible these environments should be aware of the context. Activity recognition is one of the most complex problems in context-aware environments. In this paper we propose a layered Dynamic Bayesian Network (DBN) to recognize activities in an oral presentation. The layered architecture gives us the opportunity to recognize complex activities using the classification results of sensory data in the first layer regardless of the physical environment. Our model is event-driven meaning the classification takes place only when a change has occurred in the feature space. Our contribution is that instead of developing a system for recognition of single activities with equal durations and applying it in a consecutive manner to recognize a sequence of activities, we concentrate on recognition of the whole sequence consisting of activities with different durations. The results show how DBNs can be used to overcome Hidden Markov Models problems in dealing with multiple sensory data for the classification in the second layer.