An ontology-driven ambiguous contexts mediation framework for smart healthcare applications

Abstract

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 paper, we propose a quality assured ontology-driven context mediation framework, based on efficient context-aware data fusion using 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 in a probabilistic manner. It has the ability to represent contexts according to the applications' ontology and easily composable ontological rules to mediate ambiguous contexts. We have also implemented a demonstration of the use of our model using semantic web language. Through simulation, we demonstrate the effectiveness of our proposed framework.