An Asynchronous Dynamic Bayesian Network for Activity Recognition in an Ambient Intelligent Environment

TitleAn Asynchronous Dynamic Bayesian Network for Activity Recognition in an Ambient Intelligent Environment
Publication TypeConference Paper
Year of Publication2010
AuthorsMirarmandehi, N., and H. R. Rabiee
Conference NameThe 5th International Conference on Pervasive Computing and Applications (ICPCA10)
Date Published12/2010
PublisherIEEE Press
Conference LocationMaribor, Slovenia
ISBN Number978-1-4244-9142-1
KeywordsActivity Recognition, Ambient Intelligence, Dynamic Bayesian Networks, Hidden Markov Mod- els
AbstractAmbient 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.