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Event-Driven Data Integration for Personal Health Monitoring

Alain Mouttham, Liam Peyton, Ben Eze, and Abdulmotaleb El Saddik
School of Information Technology and Engineering, University of Ottawa, Ottawa, Canada
Abstract—The emergence of biomedical wireless sensors, the wide spread use of smartphones, and advanced data stream mining techniques have enabled a new generation of personal health monitoring systems. These health monitoring systems are mostly stand-alone and are not yet integrated with existing e-Health systems, which could seriously limit their large scale deployment. In this paper, we propose an architecture for data integration within an electronic health care network based on extending the traditional SOA approach with support for complex event processing and context awareness. This architecture also facilitates integrated business performance management against quality of care targets. A detailed health monitoring scenario for the care of cardiac patients is used to illustrate system requirements and to validate the proposed architecture. The expected benefits of our approach include a higher quality of care, reduced costs for health service providers and a higher quality of life for the patients.

Index Terms—e-Health, wireless sensors, smartphone, machine learning, context awareness, data integration, event-driven architecture, complex event processing  

Cite: Alain Mouttham, Liam Peyton, Ben Eze, and Abdulmotaleb El Saddik, "Event-Driven Data Integration for Personal Health Monitoring," Journal of Emerging Technologies in Web Intelligence, Vol. 1, No. 2, pp. 110-118, November 2009. doi:10.4304/jetwi.1.2.110-118
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