Insulin Delivery Residual Analysis for Meal and Exercise Detection
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Solution Overview
Problem
Conventional insulin delivery systems, whether manual or automated, struggle to accurately predict meal and exercise events, leading to potential hypoglycemic risks due to improper insulin delivery.
Innovation Solution
A method and device that utilize a model of glucose-insulin interactions to predict meal and exercise events by calculating residuals and their rate of change over time, allowing for tailored insulin delivery adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual insulin bolus delivery is used, then the system is simple to operate, but the accuracy of insulin dosing is poor leading to hypoglycemia risk
Solution Approach 1:
The system continuously monitors blood glucose levels and uses this feedback to automatically adjust and deliver precise insulin dosages. The closed-loop control system compares actual glucose measurements with target levels and delivers correction boluses accordingly, eliminating the need for manual dosing calculations while maintaining simplicity for the user.
Solution Approach 2:
The insulin pump system autonomously performs the dosing function by automatically calculating and delivering the required insulin amount based on real-time glucose monitoring. The system serves itself by detecting glucose excursions and independently administering correction boluses without requiring user intervention in the dosing decision process.
2Measurement precision
If closed loop control system with frequent reassessment is used, then the accuracy of insulin dosing is improved, but the system complexity increases
Solution Approach 1:
The closed-loop system employs continuous feedback from glucose sensors to automatically adjust insulin delivery. The control algorithm processes glucose measurements and delivers precise correction boluses based on detected excursions, achieving high dosing accuracy through automated feedback control rather than complex manual calculations.
3Reliability
If cost function with penalties for large insulin deliveries is used, then the safety is improved by reducing hypoglycemia risk, but the productivity of glucose control is reduced due to smaller more frequent deliveries
Solution Approach 1:
The system dynamically adjusts the correction factor parameter based on the user's insulin sensitivity and current glucose state. By adapting this key parameter, the system can deliver appropriately sized correction boluses that are large enough to be efficient when needed, while the penalty function in the cost model prevents excessively large deliveries that would cause hypoglycemia, thus balancing safety and productivity.
Data Source
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AI summary
Exemplary embodiments provide an approach to predicting meal and/or exercise events for an insulin delivery system that otherwise does not otherwise identify such events. The insulin delivery system may use a model of glucose insulin interactions that projects estimated future glucose values based on the history of glucose values and insulin deliveries for the user. The predictions of meal events and/or exercise events may be based on residuals between actual glucose values and predicted glucose values. The exemplary embodiments may calculate a rate of change of the residuals over a period of time and compare the rate of change to thresholds to determine whether there likely has been a meal event or an exercise event. The insulin delivery system may then take measures to account for the meal or exercise by the user.