Energy-aware adaptation for mobile applications
ACM SIGOPS Operating Systems Review
Research challenges in wireless networks of biomedical sensors
Proceedings of the 7th annual international conference on Mobile computing and networking
The use of receiver operating characteristic curves in biomedical informatics
Journal of Biomedical Informatics - Special issue: Clinical machine learning
A framework for the automated generation of power-efficient classifiers for embedded sensor nodes
Proceedings of the 5th international conference on Embedded networked sensor systems
DynAHeal: dynamic energy efficient task assignment for wireless healthcare systems
Proceedings of the Conference on Design, Automation and Test in Europe
Detecting cocaine use with wearable electrocardiogram sensors
Proceedings of the 2013 ACM international joint conference on Pervasive and ubiquitous computing
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Current portable healthcare monitoring systems are small, battery-operated electrocardiograph devices that are used to record the heart's rhythm and activity. These on-body healthcare devices fall short on delivering real-time continuous monitoring of early detection of cardiac atrial fibrillation (A-Fib) when the symptoms last only a short period of time and require a long battery life. The focus of this paper is the design of an energy efficient model for real-time early detection of A-Fib in a wearable computing device. The design is realized by incorporating an A-Fib risk factor and a real-time A-Fib incidence-based detection algorithm. The results of the design show that the proposed energy efficient model performs better than a telemetry energy model. The design shows promising results in meeting the energy needs of real-time monitoring, detecting and reporting required in wearable computing healthcare applications.