New Research Awards
Artificial Pancreas for Use During and After Exercise (NIH)
Patients with type 1 diabetes would like to enjoy carefree and active lifestyles, conduct physical activities and exercise programs. An artificial pancreas (AP) that does not necessitate manual inputs such as meal or physical activity information from the patient can accommodate these wishes. Research on the development of AP systems that can control blood glucose levels during the physical activities of patients is important because many patients use daily physical activity as an important element of regulating their blood glucose concentrations and they participate in individual and/or group sports that dramatically change their blood glucose levels. Also, the activity patterns in several sports with bursts of high-intensity efforts are similar to children’s play and the AP system developed in the proposed work will be more conducive to the carefree play of children. To date, few research groups have attempted to test their AP technologies in an environment of exercise and none have focused on the examination of different types of activity.
An AP with properly developed control and hypoglycemia early-warning systems can be safe and effective to use both during and after a variety of types of exercise for patients with type 1 diabetes. This technology will dramatically reduce the number and duration of hypoglycemic events, as compared to the currently available methods of insulin therapy (continuous subcutaneous insulin infusions or multiple daily injections). Such AP systems can only be developed by using a sophisticated multivariable approach that includes glucose concentrations and a number of physiological variables that impact glucose homeostasis such as energy expenditure. Our multivariable recursive modeling and adaptive control framework provides the proper setting to achieve AP systems that are effective during and after a various of types of physical activities that differ markedly in energy expenditures and the metabolic systems used to support that expenditure (i.e. aerobic, anaerobic, and mixed activities, team sports). The proposed AP uses patient-specific recursive dynamic models that predict blood glucose concentrations by using subcutaneous glucose measurements and physiological data, early warning systems for hypoglycemia, and adaptive controllers based on these models to calculate insulin infusion rates.
The project aims are to develop multivariable control and hypoglycemia early warning systems for APs that will be safe to use during and after various types of exercise and group sports for patients with diabetes, to develop a multivariable simulation system to simulate the effects of different types of exercise on variations in blood glucose levels and test the algorithms developed, to assess the performance of the AP system in clinical studies at Clinical Research Centers and diabetes sports camps, and to determine the impact of the AP system developed for changes in fear of hypoglycemia, quality of life, and treatment satisfaction.
This project is a collaborative effort between Illinois Institute of Technology, University of Chicago, University of Illinois-Chicago, and York University. The collaboration between the disciplines of medicine, engineering, exercise physiology, nutrition, and the behavioral sciences combined with the “bench to bedside” design of the proposed study is consistent with the goals of translational research.
Fault-Tolerant Artificial Pancreas (NIH))
Recent research on artificial pancreas (AP) systems produced various AP technologies with good performance in clinical studies and indicated the need for improving the reliability of daily use of AP by patients. Reliable and robust AP systems can be designed by integrating multivariable monitoring, fault detection and diagnosis, sensor redundancy, and fault-tolerant control techniques to provide self-recovery, safeguards against failures and warnings and messages to users and medical care providers. This research focuses on the development of a fault-tolerant AP that integrates these technologies, mitigates the risks in AP systems, and functions at an acceptable level for BGC regulation until the diagnosed fault can be repaired or can provide safe transfer to manual operation of insulin pumps by the user.
Our research focus is on the development of multivariable algorithms and software tools for performance monitoring, fault detection and diagnosis, control system performance assessment, analytical redundancy in sensors, control algorithms with fault-tolerance and recovery, and warning systems to users and care providers to create a fault-tolerant AP system. The critical characteristics of our approach are the use of physiological variable information to complement continuous glucose measurements, multivariable modeling, monitoring, diagnosis and control techniques, and recursive models and adaptive model-based control systems. A multivariable simulator with multiple inputs (glucose concentration and physiological variables) will also be developed to test the performance monitoring, FDD, sensor redundancy, and fault-tolerant control modules and assess the performance of fault-tolerant AP system. Clinical studies will be conducted to test the performance of various modules and of fault-tolerant AP system. They will also be used to assess the potential of quality of life improvements and reduction of fear of hypoglycemia in AP use. The research is a collaboration between Ali Cinar – Illinois Institute of Technology, Elizabeth Littlejohn – University of Chicago, and Laurie Quinn – University of Illinois at Chicago, in partnership with Medtronic Corporation and BodyMedia, Inc.
Fault Detection, Diagnosis and Recovery, for Risk Mitigation in AP Systems (Juvenile Diabetes Research Foundation - JDRF)
Recent progress in AP development efforts yielded various AP system concepts that can regulate the glucose concentrations of patients with Type 1 Diabetes. The performance of these APs in clinical studies has also indicated the need for improving the reliability of APs in routine use by patients in daily activities. The adoption of AP systems by patients requires the availability of reliable AP systems that can function over long periods of time without jeopardizing the user.
An AP includes continuous glucose monitors, pumps, and infusion sets that are prone to failures. It also has software for interpreting sensor data, calculating the insulin-on-board, and computing the insulin dose to be infused by the pump. The aims of the research are to develop and test:
- A performance monitoring system to detect abnormal situations in the glucose concentration of users and in the operation of the AP system.
- A fault detection and diagnosis system to identify a fault and its source cause in the operation of the AP system by designing and integrating a controller performance assessment system, a fault detection and diagnosis system for sensors and the insulin pump, for device-body connections and for signal transmissions between the sensors, the control system and the pump
- Analytical redundancy in sensors to estimate missing data.
Recursive time series models and multivariate statistical techniques provide a powerful environment for developing these algorithms. They have been the foundation of our adaptive control systems for APs. Our research team (Ali Cinar – Illinois Institute of Technology, Elizabeth Littlejohn – University of Chicago, and Laurie Quinn – University of Illinois at Chicago) has collaborated over the past five years in the development of multivariable AP technologies that do not necessitate any meal and activity announcements for physically active young adults with T1DM. The fault detection algorithms will integrate seamlessly with our AP control algorithms. The accuracy of our models and controllers is improved by using physiological variables such as galvanic skin response, and energy expenditure reported by a sports armband in addition to glucose concentration information from a CGMS.
Our research plan includes open-loop data collection for developing simulators that include the effects of physical activity and testing of the impacts of failures in sensors and pumps in open-loop mode in a clinical study at UIC, development of these multivariable simulators, design and development of performance monitoring, fault detection and diagnosis, and analytical redundancy algorithms, and testing them in simulations at IIT, and testing the algorithms in closed-loop AP setting in a clinical study at UC.