Abstract: “Prerequisite to the better living of an insulin dependent diabetes mellitus (IDDM) or type-1 diabetic patients is the closed loop blood glucose regulation via subcutaneous insulin infusion and continuous glucose monitoring system (SC-SC route). Closed loop control for blood glucose level in a diabetic patient necessarily uses an explicit model of the process. A fixed parameter full order or reduced order model does not characterize the inter-patient and intra-patient parameter variability. This paper deals with a real time implementation of online identification of frequency domain kernels from the input output data of an IDDM patient. The data-driven model of the patient is identified in real time by solving Volterra kernels up to second order using adaptive recursive least square (ARLS) algorithm with a short memory length of M=2. The frequency domain kernels, or the Volterra transfer function (VTF) are computed by taking the FFTs on respective time domain kernels for a specific leng”

Keywords: diabetes mellitus, identification, nonparametric model, Volterra kernels, hardware realization