Title

SME2EM: Smart mobile end-to-end monitoring architecture for life-long diseases

RIS ID

104402

Publication Details

Serhani, M. Adel., El Menshawy, M. & Benharref, A. 2016, 'SME2EM: Smart mobile end-to-end monitoring architecture for life-long diseases', Computers in Biology and Medicine, vol. 68, pp. 137-154.

Abstract

Monitoring life-long diseases requires continuous measurements and recording of physical vital signs. Most of these diseases are manifested through unexpected and non-uniform occurrences and behaviors. It is impractical to keep patients in hospitals, health-care institutions, or even at home for long periods of time. Monitoring solutions based on smartphones combined with mobile sensors and wireless communication technologies are a potential candidate to support complete mobility-freedom, not only for patients, but also for physicians. However, existing monitoring architectures based on smartphones and modern communication technologies are not suitable to address some challenging issues, such as intensive and big data, resource constraints, data integration, and context awareness in an integrated framework. This manuscript provides a novel mobile-based end-to-end architecture for live monitoring and visualization of life-long diseases. The proposed architecture provides smartness features to cope with continuous monitoring, data explosion, dynamic adaptation, unlimited mobility, and constrained devices resources. The integration of the architecture's components provides information about diseases' recurrences as soon as they occur to expedite taking necessary actions, and thus prevent severe consequences. Our architecture system is formally model-checked to automatically verify its correctness against designers' desirable properties at design time. Its components are fully implemented as Web services with respect to the SOA architecture to be easy to deploy and integrate, and supported by Cloud infrastructure and services to allow high scalability, availability of processes and data being stored and exchanged. The architecture's applicability is evaluated through concrete experimental scenarios on monitoring and visualizing states of epileptic diseases. The obtained theoretical and experimental results are very promising and efficiently satisfy the proposed architecture's objectives, including resource awareness, smart data integration and visualization, cost reduction, and performance guarantee.

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Link to publisher version (DOI)

http://dx.doi.org/10.1016/j.compbiomed.2015.11.009