Degree Name

MS (Master of Science)

Program

Computer and Information Sciences

Date of Award

12-2019

Committee Chair or Co-Chairs

Ferdaus Kawsar

Committee Members

Phil Pfeiffer, Brian Bennett, Stephen Hendrix

Abstract

A novel, interactive Android app has been developed that monitors the health of type 2 diabetic patients in real-time, providing patients and their physicians with real-time feedback on all relevant parameters of diabetes. The app includes modules for recording carbohydrate intake and blood glucose; for reminding patients about the need to take medications on schedule; and for tracking physical activity, using movement data via Bluetooth from a pair of wearable insole devices. Two machine learning models were developed to detect seven physical activities: sitting, standing, walking, running, stair ascent, stair descent and use of elliptical trainers. The SVM and decision tree models produced an average accuracy of 85% for these seven activities. The decision tree model is implemented in an app that classifies human activity in real-time.

Document Type

Thesis - Withheld

Copyright

Copyright by the authors.

Available for download on Friday, August 18, 2023

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