Search results
(21 - 22 of 22)
Pages
- Title
- Incorporating Real-Time Estimates of Physiological States in Artificial Pancreas Systems
- Creator
- Sevil, Mert
- Date
- 2020
- Description
-
Type-1 diabetes is a chronic disease that has a negative impact on the life of a person with diabetes causing other chronic diseases, reducing...
Show moreType-1 diabetes is a chronic disease that has a negative impact on the life of a person with diabetes causing other chronic diseases, reducing the quality of life, and the possibility of causing dangerous reductions in blood glucose levels that may lead to coma or death. More than 100 million U.S. adults are now living with diabetes or pre-diabetes. Diabetes is one of the most expensive public health problems in the U.S. at $327 billion in 2017. Thus, alternative solutions or novel proposals are crucial to more effective treatments and cure. Artificial pancreas systems are one of the common treatment techniques of Type-1 Diabetes, which reduce the risk of diabetes-related complications and make diabetics' lives easier and make it convenient. Artificial pancreas systems aim to maintain blood glucose concentrations in a tighter target blood glucose range, which is a challenging problem. Several factors affect blood glucose concentrations including intensity of exercise, type of exercise, acute psychological stress and the physical state of a person with diabetes. These factors are unknown disturbances for artificial pancreas control systems. In this project, a single non-invasive wrist-worn device is used to obtain different biosignals in-real time. Biosignals are utilized with the development energy expenditure estimation model, psychological stress detection and physical state classification models. Several machine learning methods are tested and validated until the best classification and estimation accuracy is achieved for each estimate. Obtained models are incorporated with the current artificial pancreas design to improve its glycemic control performance. The controller is aware of such measurable disturbances with the proposed method, which allows for providing accurate and timely control action. Additional estimates are utilized to improve blood glucose concentration prediction model accuracy. Clinical trials are used to test and validate the proposed work. In summary, the presented work illustrates different machine learning techniques and algorithms that can enhance automated insulin delivery by a multivariable artificial pancreas system and enhance the quality of life of people with Type 1 diabetes.
Show less
- Title
- Developing Adaptive and Predictive Modules for the Second Generation of Multivariable Insulin Delivery System for People with Type-1 Diabetes
- Creator
- Askari, Mohammad Reza
- Date
- 2023
- Description
-
In this research, we are developing the second generation of multivariable automated insulin delivery system (mvAID) for people with Type 1...
Show moreIn this research, we are developing the second generation of multivariable automated insulin delivery system (mvAID) for people with Type 1 diabetes (T1D). AID system is improved by integrating missing data from sensors into the system, reconciling outliers in the data, and eliminating the effects of artifacts in signals from wearable devices. Behavioral patterns of individuals with T1D are captured by data-driven models. The model predictive control algorithm of the mvAID uses these patterns for making decisions and predicting glucose concentrations in the future more accurately. A pipeline algorithm is developed for removing noise and motion artifacts from wristband signals. Then, energy expenditure, physical activity, and acute psychological stress (APS) are estimated from wearable device signals to detect and quantify disturbances affecting the concentration of blood glucose concentration. Additionally, different modules were designed for predicting risky glycemic episodes and are used to build the second generation of the mvAID system. The techniques developed are tested with historical data sets from various clinical experiments and free-living data, and with simulations made by using our multivariable glucose, insulin and physiological variables simulator (mGIPsim).
Show less