Conference Paper

A Middleware Framework for Ambiguous Context Mediation in Smart Healthcare Application.

Univ. of Texas at Arlington, Arlington;
DOI: 10.1109/WIMOB.2007.4390866 Conference: Third IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, WiMob 2007, White Plains, New York, USA, 8-10 October 2007, Proceedings
Source: DBLP

ABSTRACT Ubiquitous healthcare applications envision future computing and networking environments as being filled with sensors that can determine various types of contexts of its inhabitants, such as location, activity and vital signs. While such information is useful in providing context-sensitive services to the inhabitants to promote intelligent independent living, however in reality, both sensed and interpreted contexts may often be ambiguous. Thus, a challenge facing the development of realistic and deployable context-aware services is the ability to handle ambiguous contexts to prevent hazardous situations. In this paper, we propose a framework which supports efficient context-aware data fusion for healthcare applications that assume contexts could be ambiguous. Our framework provides a systematic approach to derive context fragments, and deal with context ambiguity in a probabilistic manner. We also incorporate the ability to represent contexts within the applications, and the ability to easily compose rules to mediate ambiguous contexts. Through simulation and analysis, we demonstrate the effectiveness of our proposed framework for monitoring elderly people in the smart home environment.

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    ABSTRACT: Pervasive computing applications often involve sensor-rich networking environments that capture various types of user contexts such as locations, activities, vital signs, and so on. Such context information is useful in a variety of applications, for example, monitoring health information to promote independent living in "aging-in-place” scenarios, or providing safety and security of people and infrastructures. In reality, both sensed and interpreted contexts are often ambiguous, thus leading to potentially dangerous decisions if not properly handled. Therefore, a significant challenge in the design and development of realistic and deployable context-aware services for pervasive computing applications lies in the ability to deal with ambiguous contexts. In this paper, we propose a resource-optimized, quality-assured context mediation framework for sensor networks. The underlying approach is based on efficient context-aware data fusion, information-theoretic reasoning, and selection of sensor parameters, leading to an optimal state estimation. In particular, we apply dynamic Bayesian networks to derive context and deal with context ambiguity or error in a probabilistic manner. Experimental results using SunSPOT sensors demonstrate the promise of this approach.
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    ABSTRACT: Pervasive computing applications envision sensor rich computing and networking environments that can capture various types of contexts of inhabitants of the environment, such as their locations, activities, vital signs, and environmental measures. Such context information is useful in a variety of applications, for example to manage health information to promote independent living in “aging-in-place” scenarios. In reality, both sensed and interpreted contexts are often ambiguous, leading to potentially dangerous decisions if not properly handled. Thus, a significant challenge facing the development of realistic and deployable context-aware services for pervasive computing applications is the ability to deal with these ambiguous contexts. In this paper, we propose a resource optimized quality assured context mediation framework for resource constrained sensor networks based on efficient context-aware data fusion and information theoretic sensor parameter selection for optimal state estimation. The proposed framework provides a systematic approach based on dynamic Bayesian networks to derive context fragments and deal with context ambiguity or error in a probabilistic manner. Experimental results using SunSPOT sensors demonstrate the promise of this approach.
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    ABSTRACT: Ubiquitous (or smart) healthcare applications en-vision 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 resource optimized quality assured context mediation framework for resource constrained sensor networks based on efficient context-aware data fusion and information theoretic system parameter selection for optimal state estimation. The proposed framework provides a systematic approach based on dynamic Bayesian networks 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 requirements. Experimental results using Sun SPOT and other sensors demonstrate the promise of this approach.

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May 28, 2014