Abstract
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 con- textual information for several reasons. First, low-level data from multisensors have different degrees of uncertainty. Second, gen- erated contexts can be conflicting, even though they are acquired by simultaneous operations. We propose the static evidential fu- sion 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 ap- proach 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.
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