Empirical Dynamic Model Identification for Blood-Glucose Dynamics in Response to Physical Activity

Isuru S Dasanayake; Dale E Seborg; Jordan E Pinsker; Francis J Doyle 3rd; Eyal Dassau

doi:10.1109/CDC.2015.7402815

Empirical Dynamic Model Identification for Blood-Glucose Dynamics in Response to Physical Activity

Proc IEEE Conf Decis Control. 2015 Dec:2015:3834-3839. doi: 10.1109/CDC.2015.7402815.

Authors

Isuru S Dasanayake¹, Dale E Seborg¹, Jordan E Pinsker², Francis J Doyle 3rd³, Eyal Dassau³

Affiliations

¹ Department of Chemical Engineering, University of California Santa Barbara, Santa Barbara, CA 93106-5080, USA; William Sansum Diabetes Center, Santa Barbara, CA 93105, USA.
² William Sansum Diabetes Center, Santa Barbara, CA 93105, USA.
³ William Sansum Diabetes Center, Santa Barbara, CA 93105, USA; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA.

Abstract

In this paper, the dynamic response of blood glucose concentration in response to physical activity of people with Type 1 Diabetes Mellitus (T1DM) is captured by subspace identification methods. Activity (input) and subcutaneous blood glucose measurements (output) are employed to construct a personalized prediction model through semi-definite programming. The model is calibrated and subsequently validated with non-overlapping data sets from 15 T1DM subjects. This preliminary clinical evaluation reveals the underlying linear dynamics between blood glucose concentration and physical activity. These types of models can enhance our capabilities of achieving tighter blood glucose control and early detection of hypoglycemia for people with T1DM.

Abstract

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