A framework for evidential-reasoning systems
Readings in uncertain reasoning
Combining belief functions when evidence conflicts
Decision Support Systems
Modeling Context Information in Pervasive Computing Systems
Pervasive '02 Proceedings of the First International Conference on Pervasive Computing
Recognizing User Context via Wearable Sensors
ISWC '00 Proceedings of the 4th IEEE International Symposium on Wearable Computers
Reasoning about Uncertain Contexts in Pervasive Computing Environments
IEEE Pervasive Computing
Resolving uncertainty in context integration and abstraction: context integration and abstraction
Proceedings of the 5th international conference on Pervasive services
Accurate activity recognition in a home setting
UbiComp '08 Proceedings of the 10th international conference on Ubiquitous computing
Managing Context Information in Mobile Devices
IEEE Pervasive Computing
Evidential fusion of sensor data for activity recognition in smart homes
Pervasive and Mobile Computing
A context quality model to support transparent reasoning with uncertain context
QuaCon'09 Proceedings of the 1st international conference on Quality of context
Activity recognition using temporal evidence theory
Journal of Ambient Intelligence and Smart Environments
Identifying important action primitives for high level activity recognition
EuroSSC'10 Proceedings of the 5th European conference on Smart sensing and context
Multi-sensor data fusion within the belief functions framework: application to smart home services
NEW2AN'11/ruSMART'11 Proceedings of the 11th international conference and 4th international conference on Smart spaces and next generation wired/wireless networking
Review: Situation identification techniques in pervasive computing: A review
Pervasive and Mobile Computing
An evidential fusion approach for activity recognition in ambient intelligence environments
Robotics and Autonomous Systems
Journal of Ambient Intelligence and Smart Environments - Design and Deployment of Intelligent Environments
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In the domain of ubiquitous computing, the ability to identify the occurrence of situations is a core function of being 'context-aware'. Given the uncertain nature of sensor information and inference rules, reasoning techniques that cater for uncertainty hold promise for enabling the inference process. In our work, we apply the Dempster Shafer theory of evidence to infer situation occurrence with minimal use of training data. We describe a set of evidential operations for sensor mass functions using context quality and evidence accumulation for continuous situation detection. We demonstrate how our approach enables situation inference with uncertain information using a case study based on a published smart home activity data set.