Date of Award

Spring 1-1-2016

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Electrical, Computer & Energy Engineering

First Advisor

Li Shang

Second Advisor

Jae-Woong Jeong

Third Advisor

Michael Lightner

Fourth Advisor

Gary Phillis

Fifth Advisor

Jianliang Xiao

Abstract

Design for human-borne sensing faces a key challenge: to provide increasingly high-quality, day-by-day sensing accuracy and reporting from an energy-constrained and aggressively miniaturized computing form factor. Long-term maintenance-free operation is an another important goal for devices intended to be carried by people throughout their daily life. The human sensor form factor is driven by its energy storage requirements, hence power consumption resulting from data sensing, processing, and communication.

This thesis studies the energy costs in the full end-to-end human sensor platform, however specific attention is paid to optimizing energy use in the worn sensor device. Three computing layers comprising the human sensor platform are examined: the human sensor device, the mobile data aggregator, including smart phone and smart watch, and cloud-side data warehousing. The heterogeneous compute and energy capacity qualities of the layers are exploited for both intra-layer and cross-layer improvements in energy efficiency. Opportunities to offload power consumption from the sensor device, thus enabling smaller battery capacity and further scaling of sensor device form factor are prioritized. The full data handling flow, including data sensing, data cleaning, feature extraction and classification, data communications and storage, is considered, and tradeoffs between computed result accuracy and energy cost are tailored across a range of applications.

Wearable human sensor applications implemented and reported on in this thesis include mobile online gait analysis for runners, grocery store aisle localization with augmented reality driven item recommendation, and wearable in-field electroencephalographic brain sensing. Results include improvements in energy-efficiency over the state-of-the-art, including an 11X speedup in cloud data processing, a 47% power reduction in a wearable running sensor when applying a smartphone-to-wearable collaboration, and, most significantly, a one-order-of-magnitude power reduction when applying an event-driven sparse adaptive sampling method to a wearable human running gait analysis sensor.

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