On the Dempster-Shafer framework and new combination rules
Information Sciences: an International Journal
Some computer science issues in ubiquitous computing
Communications of the ACM - Special issue on computer augmented environments: back to the real world
A Bayesian model of plan recognition
Artificial Intelligence
Artificial Intelligence
Combining belief functions when evidence conflicts
Decision Support Systems
Communications of the ACM - The disappearing computer
International Journal of Approximate Reasoning
A probabilistic plan recognition algorithm based on plan tree grammars
Artificial Intelligence
Decision making in the TBM: the necessity of the pignistic transformation
International Journal of Approximate Reasoning
Human Activity Recognition and Pattern Discovery
IEEE Pervasive Computing
Using Dempster-Shafer theory of evidence for situation inference
EuroSSC'09 Proceedings of the 4th European conference on Smart sensing and context
6LoWPAN: The Wireless Embedded Internet
6LoWPAN: The Wireless Embedded Internet
IE '10 Proceedings of the 2010 Sixth International Conference on Intelligent Environments
Proceedings of the 2011 international workshop on Situation activity & goal awareness
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In Smart Home, understanding the environment and what is going on is the basis of all adapted services. Unfortunately, inferring situations and activity recognition directly from raw data is way too complex to be applied. Firstly, we present a layered architecture we are building to process raw data into abstract situations and activities. Secondly, data fusion tools using the belief functions theory are introduced as a general framework to provide a first level of abstraction from raw data given by sensors to a more complex context model. Then a methodology to apply the model to our Smart Home within the belief functions framework, a first implementation and the encountered issues in modeling are discussed.