Adaptive personalized multivariable artificial pancreas using plasma insulin estimates

Abstract

An adaptive and personalized multivariable artificial pancreas (mAP) system using plasma insulin estimates is proposed to efficiently accommodate major disturbances to the blood glucose concentration, such as meal and physical activity. Accurate adaptive glycemic models are developed through a recursive subspace identification technique with wearable physiological measurements and estimates of unannounced meal effect and plasma insulin concentration (PIC) along with continuous glucose concentration signals to characterize the glucose concentration dynamics under various conditions such as food consumption and physical activity. The identified models with time-varying parameters are employed in the design of an adaptive model predictive control (MPC) system that is cognizant of the PIC. The adaptive controller parameters, dynamic PIC constraint, addition of physiological measurements from wearable devices, feature variables generated from the glucose measurements, and estimation of uncertain model parameters, including the meal effect, enable the mAP system to effectively compute the optimal insulin infusion over diverse diurnal variations without meal and exercise announcements. Simulation case studies using a multivariable simulator demonstrate the efficacy of the proposed mAP system.