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Continuous Blood Glucose (CGM) monitors have revolutionized the ability ofdiabetics to manage their blood glucose, and paved the way for artificialpancreas systems. In this paper we augment CGM data with sensor input collectedby a smart phone and use it to provide analytical tools for patients andclinicians. We collected GPS data, activity classifications, and blood glucosedata with a custom iOS application over a 9 month period from a singlefree-living type-1 diabetic patient. This data set is novel in terms of it'ssize, the inclusion of GPS data, and the fact that it was collectednon-intrusively from a free-living patient. We describe a method to measure theoccurrence of lifestyle \textit{events} based on GPS and activity data, andshow that they can capture instances of food consumption and are thereforecorrelated to changes in blood glucose. Finally, we incorporate these eventrepresentations into our system to create useful visualizations andnotifications to aid patients in managing their diabetes.

Personalized Understanding of Blood Glucose Dynamics via Mobile Sensor Data