Modified control algorithms for patients with type 1 diabetes mellitus undergoing exercise

Abstract

In this article, two previously published control algorithms for type 1 diabetes mellitus (T1DM) are modified for patients undergoing exercise. These two control algorithms include the hybrid neural network model predictive control employed on continuous subcutaneous insulin infusion (CSII) therapy, and the fuzzy-logic-based supervisor applied to multiple daily injections (MDI) therapy. A simulation model incorporating a well-acknowledged meal glucose–insulin model and an additional model of physical activity is employed to create virtual subjects for testing. The main notion of the modifications of the control algorithms is to subtract extra carbohydrate for aerobic exercise (ExCarbs) to prevent the exercise-induced hypoglycemia. This residual of CHO, after subtracting ExCarbs from an estimated carbohydrate (CHO) of a meal, is a more appropriate quantity of the CHO which should be compensated by external insulin. The two modified control algorithms are tested for subjects with various intensities and durations of exercise. Simulation results of the virtual subjects show that the algorithms are effective to prevent exercise-induced hypoglycemia. In addition, the simulation results also reveal that the modified methodologies are still robust with blood glucose (BG) level of subjects maintaining within safe region.