Sensors Based Optimized Closed Loop Control Algorithm to Minimize Hypoglycemia/Hyperglycemia using 4-Variate Time Series Data

The goal of this work is to create a closed-loop control system that combines continuous glucose, carbohydrates, and physiological variable readings to regulate glucose levels to treat hyperglycemia and prevent hypoglycemia, as well as a hypoglycemia early alarm module.We use an Extended Kalman filter (EKF) to estimate time-series coefficients of type 1 diabetic patient's glucose levels using a 4-variate time series data such as glucose level, insulin dose, physical activities, and food consumption.An adaptive Kalman Filter algorithm is best suitable for sensor fusion and also for real-time data, which uses the series of measurement data over the period to tend to predict the unknown variable.We proposed to provide the mealtime bolus as a prolonged bolus, with a slice of the insulin dosage delivered before food and the remainder delivered after food.Our research entails the use of a glucose monitor (CGM), physical activity monitor (accelerometer sensor), carbohydrates monitor (biosensor), an automatic insulin infusion controller that calculates the quantity of insulin to be injected, and an insulin infusion drive.Low-Power and Lossy Routing (LPLR) with 6LoWPAN are proposed for efficient routing and private networks.Our model is evaluated using the UVa/Padova simulator for 25 patients.We conducted our experiments with 25 virtual patients with a mix of all age categories.The simulator's default sample time is one minute, but we have set the 5-minute sampling time.Overall, the proposed models are good at predicting hypoglycaemic (<70 mg/dl), normal glycaemic (>70 and 180 mg/dl), and hyperglycaemic (180 mg/dl) blood sugar levels.