Artificial Intelligence
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
Reasoning about Uncertain Contexts in Pervasive Computing Environments
IEEE Pervasive Computing
The Role of Probabilistic Schemes in Multisensor Context-Awareness
PERCOMW '07 Proceedings of the Fifth IEEE International Conference on Pervasive Computing and Communications Workshops
Issues in data fusion for healthcare monitoring
Proceedings of the 1st international conference on PErvasive Technologies Related to Assistive Environments
Evidential fusion of sensor data for activity recognition in smart homes
Pervasive and Mobile Computing
Sensor Data Fusion Using DSm Theory for Activity Recognition under Uncertainty in Home-Based Care
AINA '09 Proceedings of the 2009 International Conference on Advanced Information Networking and Applications
A classification and modeling of the quality of contextual information in smart spaces
PERCOM '09 Proceedings of the 2009 IEEE International Conference on Pervasive Computing and Communications
A context-aware service platform to support continuous care networks for home-based assistance
UAHCI'07 Proceedings of the 4th international conference on Universal access in human-computer interaction: ambient interaction
An approach to data fusion for context awareness
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
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In home-based care, reliable contextual information of remotely monitored patients should be generated by correctly recognizing the activities to prevent hazardous situations of the patient. It is difficult to achieve a higher confidence level of contextual information for several reasons. First, low-level data from multisensors have different degrees of uncertainty. Second, generated contexts can be conflicting, even though they are acquired by simultaneous operations. We propose the static evidential fusion process (SEFP) as a context-reasoning method. The context-reasoning method processes sensor data with an evidential form based on the Dezert-Smarandache theory (DSmT). The DSmT approach reduces ambiguous or conflicting contextual information in multisensor networks. Moreover, we compare SEFP based on DSmT with traditional fusion processes such as Bayesian networks and the Dempster-Shafer theory to understand the uncertainty analysis in decision making and to show the improvement of the DSmT approach compared to the others.