MS (Master of Science)
Computer and Information Sciences
Date of Award
Committee Chair or Co-Chairs
Phil Pfeiffer, Brian Bennett, Stephen Hendrix
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.
Thesis - unrestricted
Chowdhury, Nusrat, "Design and Development of a Comprehensive and Interactive Diabetic Parameter Monitoring System - BeticTrack" (2019). Electronic Theses and Dissertations. Paper 3646. https://dc.etsu.edu/etd/3646
Copyright by the authors.