A meal detection and meal-size estimation algorithm is developed for use in artificial pancreas (AP) control systems for people with type 1 diabetes. The algorithm detects the consumption of a meal and estimates its carbohydrate (CHO) amount to determine the appropriate dose of insulin bolus for a meal. It can be used in AP systems without manual meal announcements, or as a safety feature for people who may forget entering meal information manually. Using qualitative representation of the filtered continuous glucose monitor signal, a time period labeled as meal flag is identified. At every sampling time during this time period, a fuzzy system estimates the amount of CHO. Meal size estimator uses both glucose sensor and insulin data. Meal insulin bolus is based on estimated CHO. The algorithm does not change the basal insulin rate. Thirty in silico subjects of the UVa/Padova simulator are used to illustrate the performance of the algorithm. For the evaluation dataset, the sensitivity and false positives detection rates are 91.3% and 9.3%, respectively, the absolute error in CHO estimation is 23.1%, the mean blood glucose level is 142 mg/dl, and glucose concentration stays in target range (70-180 mg/dl) for 76.8% of simulation duration on average.

Integration of a meal detection module in an AP system is a further step toward an automated AP without manual entries. Detection of a consumed meal/snack and infusion of insulin boluses using an estimate of CHO enables the AP system to automatically prevent postprandial hyperglycemia.

The accurate real-time estimation of PIC will benefit the AP systems by preventing overdelivery of insulin when significant insulin is present in the bloodstream.

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In this work the real-time estimation of plasma insulin concentration (PIC) to quantify the insulin in the bloodstream in patients with type 1 diabetes mellitus (T1DM) is presented. To this end, Hovorka's model, a glucose−insulin dynamics model, is incorporated with various estimation techniques, including continuous-discrete extended Kalman filtering, unscented Kalman filtering, and moving horizon estimation, to provide an estimate of PIC. Furthermore, due to the considerable variability in the temporal dynamics of patients, some uncertain model parameters that have significant effects on PIC estimates are considered as additional states in Hovorka's model to be simultaneously estimated. Latent variable regression models are developed to individualize the PIC estimators by appropriately initializing the time-varying model parameters for improved convergence. The performance of the proposed methods is evaluated using clinical data sets from subjects with T1DM, and the results demonstrate the accurate estimation of PIC.