MediAlly: A provenance-aware remote health monitoring middleware

This paper presents MediAlly, a middleware for supporting energy-efficient, long-term remote health monitoring. Data is collected using physiological sensors and transported back to the middleware using a smart phone. The key to MediAlly's energy efficient operations lies in the adoption of an...

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Published in2010 IEEE International Conference on Pervasive Computing and Communications : (PerCom 2010), Mannheim, Germany, 29 March - 2 April, 2010 pp. 125 - 134
Main Authors Chowdhury, Atanu Roy, Falchuk, Ben, Misra, Archan
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.03.2010
Subjects
Biomedical monitoring
Context modeling
Context-aware services
Energy efficiency
Functional programming
Handheld computers
Middleware
Paper technology
Remote monitoring
Sensor phenomena and characterization
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Summary:This paper presents MediAlly, a middleware for supporting energy-efficient, long-term remote health monitoring. Data is collected using physiological sensors and transported back to the middleware using a smart phone. The key to MediAlly's energy efficient operations lies in the adoption of an Activity Triggered Deep Monitoring (ATDM) paradigm, where data collection episodes are triggered only when the subject is determined to possess a specified context. MediAlly supports the on-demand collection of contextual provenance using a novel low-overhead provenance collection sub-system. The behaviour of this sub-system is configured using an application-defined context composition graph. The resulting provenance stream provides valuable insight while interpreting the `episodic' sensor data streams. The paper also describes our prototype implementation of MediAlly using commercially available devices.
ISBN:9781424453290
1424453291
DOI:10.1109/PERCOM.2010.5466985