Date of Award

Spring 1-1-2013

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Richard Han

Second Advisor

Shivakant Mishra

Third Advisor

James Martin

Fourth Advisor

Qin Lv

Fifth Advisor

David Chu

Abstract

We demonstrate the feasibility of constructing a mobile cloud system that efficiently, conveniently and accurately fuses multimodal smartphone sensor data to identify and log unusual personal events in mobile users' daily lives. Our myBlackBox system is designed to leverage a smartphone as a personalized blackbox-like recorder. In the system, we develop new location-based classifiers for audio and accelerometer that are personalized and noise-resistant. The system incorporates a hybrid architectural design that combines unsupervised classification of audio, accelerometer and location data with supervised joint fusion classification to achieve good accuracy, customization, convenience and scalability. We identify the best supervised learning algorithm for fusing together multi-modal mobile sensor data for unusual event identification and characterize its improvement in accuracy over location-based audio and activity classifiers. Finally, we show the feasibility of the myBlackbox concept by implementing and evaluating an end-to-end system that combines Android smartphones with a cloud server over a deployment consisting of fifteen users for over a one month period.

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