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PhD Defense: Power Optimization for Medical Sensing Systems

Name:  Jun Luan

Location:  EH 4106

Date: February 24, 2016

Time: 1:00pm

Committee: Pai Chou (Chair), Mohammad Al Faruque, Fadi Kurdahi


Medical sensing systems collect and analyze the patients’ physiological data for monitoring, aid or diagnostic purposes. System designers are faced with stringent requirements on not only correctness and safety but also power. Reference designs and multi-purpose platforms help to significantly shorten the development cycle.

This work takes a cross-layer, system-level, platform-based approach to addressing the problem of saving power in a class of portable medical system. We propose a low-power medical sensing system that can be used to monitor Electrocardiography (ECG), Photo- plethysmogram (PPG), and muscle tension. It also includes a hand gesture recognition system to aid mobility-impaired patients.

We explore the theory and application of a compressive sensing framework to medical signal processing. A novel compressive sensing-based ECG compression algorithm and a dominant frequency extraction-based PPG heart-rate calculation algorithm are proposed to reduce the system power. The unique combination of hardware structure and software signal-processing algorithms makes low-power design possible. The system test results show that the proposed system is superior to existing works in terms of power consumption and system size.