Resolving and mediating ambiguous contexts for pervasive care environments

Ubiquitous (or smart) healthcare applications envision sensor rich computing and networking environments that can capture various types of contexts of patients (or inhabitants of the environment), such as their location, activities and vital signs. Such context information is useful in providing health related and wellness management services in an intelligent way so as to promote independent living. However, in reality, both sensed and interpreted contexts may often be ambiguous, leading to fatal decisions if not properly handled. Thus, a significant challenge facing the development of realistic and deployable context-aware services for healthcare applications is the ability to deal with ambiguous contexts to prevent hazardous situations. In this work, we propose a quality assured context mediation framework, based on efficient context-aware data fusion and information theoretic system parameter selection for optimal state estimation in resource constrained sensor network. The proposed framework provides a systematic approach based on dynamic Bayesian network to derive context fragments and deal with context ambiguity or error in a probabilistic manner. It has the ability to incorporate context representation according to the applications' quality requirement. Experimental results using SunSPOT sensors demonstrate the promise of this approach.