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Sensor faults in an artificial pancreas (AP) system for people with type 1 diabetes (T1D) can yield insulin infusion rates that may cause hypoglycemia or hyperglycemia. New statistical process monitoring methods are proposed for real-time detection of sensor-related faults in AP systems to mitigate their effects. Remodulated dynamic time warping for synchronization of signal trajectories and Savitzky-Golay filter for calculation of real-time numerical derivatives are integrated with multiway principal component analysis. Data from 14 subjects that participated in 60 hours of closed-loop AP experiments with variations in meals and physical activity levels and times are used. Glucose measurements from a continuous glucose monitoring sensor are monitored for fault detection. The results illustrate that the proposed method is able to detect various types of unexpected dynamical changes or faults, and label them correctly. There were no missed faults in any tested cases. The algorithm can inform AP users about sensor faults and unexpected changes, and provides valuable information to an AP for prevention hypoglycemia or hyperglycemia that may be caused by sensor faults.

In this work, an artificial pancreas (AP) system, implemented on a mobile device is described. The proposed AP platform integrates hardware (insulin pump, glucose monitor, various sensors for vital signs and physical activities) and software (closed-loop control algorithm, sensor fusion, data storage and remote server access) components via smartphone that is running a dedicated Operating System designed for AP systems. Interfacing with this OS and custom application development steps are presented. Closed loop operation is demonstrated with case studies.

Artificial pancreas (AP) control systems rely on signals from glucose sensors to collect glucose concentration (GC) information from people with Type 1 diabetes and compute insulin infusion rates to maintain GC within a desired range Sensor performance is often limited by sensor errors, communication interruptions and noise A hybrid online sensor error detection and functional redundancy system is developed to detect errors in online signals, and replace erroneous or missing values detected with model-based estimates. The proposed hybrid system relies on two techniques, an outlier-robust Kalman filter (ORKF) and a locally-weighted partial least squares (LW-PLS) regression model. This leverages the advantages of automatic measurement error elimination with ORKF and data-driven prediction with LW-PLS. A novel method called nominal angle analysis is proposed to distinguish between signal faults and large changes in sensor values caused by real dynamic changes in the metabolism The performance of the system is illustrated with clinical data from continuous glucose monitoring sensors collected from people with Type 1 diabetes.

Post-prandial hyperglycemia can occur more frequently in artificial pancreas control systems that do not have meal announcements that trigger insulin boluses. Meal announcements are manual feedforward inputs to the artificial pancreas. We have developed a meal-detection and meal bolusing algorithm based on continuous glucose measurements that does not require any manual information from patients. Bergman's minimal model is modified and used in an Unscented Kalman filter for state estimations. The estimated rate of appearance of glucose is used for meal detection and calculation of meal boluses. The proposed algorithm is tested on the UVa/Padova metabolic simulator. The results indicate that the proposed algorithm decreases the frequency, duration and magnitude of hyperglycemia significantly without causing any additional hypoglycemia. The algorithm is developed as a new module of an integrated multivariable adaptive artificial pancreas control system.

An artificial pancreas (AP) system with a hypoglycemia early alarm system and adaptive control system based on multivariable recursive time series models is developed. The inputs of the model include glucose concentration (GC) and physiological signals that provide information about the physical activities and stress of the patient. The stability of the recursive time-series models is guaranteed by a constrained optimization method. Generalized predictive control (GPC) is used to regulate GC. Experiments in a clinical setting illustrate the performance of the AP and compare it to open-loop regulation by the patient. Results show that the AP can regulate GC successfully and prevent hypoglycemia in spite of exercise.

Artificial pancreas (AP) systems offer an important improvement in regulating blood glucose concentration for patients with type 1 diabetes, compared to current approaches. AP consists of sensors, control algorithms and an insulin pump. Different AP control algorithms such as proportional-integral-derivative, model-predictive control, adaptive control, and fuzzy logic control have been investigated in simulation and clinical studies in the past three decades. The variability over time and complexity of the dynamics of blood glucose concentration, unsteady disturbances such as meals, time-varying delays on measurements and insulin infusion, and noisy data from sensors create a challenging system to AP. Adaptive control is a powerful control technique that can deal with such challenges. In this paper, a review of adaptive control techniques for blood glucose regulation with an AP system is presented. The investigations and advances in technology produced impressive results, but there is still a need for a reliable AP system that is both commercially viable and appealing to patients with type 1 diabetes.