Computer Engineering Commons^™

A Data-Driven Behavior Modeling And Analysis Framework For Diabetic Patients On Insulin Pumps, Sanjian Chen, Lu Feng, Michael Rickels, Amy Peleckis, Oleg Sokolsky, Insup Lee

Oleg Sokolsky

About 30%-40% of Type 1 Diabetes (T1D) patients in the United States use insulin pumps. Current insulin infusion systems require users to manually input meal carb count and approve or modify the system-suggested meal insulin dose. Users can give correction insulin boluses at any time. Since meal carbohydrates and insulin are the two main driving forces of the glucose physiology, the user-specific eating and pump-using behavior has a great impact on the quality of glycemic control.

In this paper, we propose an “Eat, Trust, and Correct” (ETC) framework to model the T1D insulin pump users’ behavior. We use machine learning …

Linking Abstract Analysis To Concrete Design: A Hierarchical Approach To Verify Medical Cps Safety, Anitha Murugesan, Oleg Sokolsky, Sanjai Rayadurgam, Michael Whalen, Mats Heimdahl, Insup Lee

Oleg Sokolsky

Complex cyber-physical systems are typically hierarchically organized into multiple layers of abstraction in order to manage design complexity and provide verification tractability. Formal reasoning about such systems, therefore, necessarily involves the use of multiple modeling formalisms, verification paradigms, and concomitant tools, chosen as appropriate for the level of abstraction at which the analysis is performed. System properties verified using an abstract component specification in one paradigm must then be shown to logically follow from properties verified, possibly using a different paradigm, on a more concrete component description, if one is to claim that a particular component when deployed in the …