Hybrid Controller IMC Loop for Automatic Glucose Correction
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Solution Overview
Problem
Current artificial pancreas systems require patient intervention for meal and exercise announcements, leading to performance degradation due to estimation errors, bolusing delays, or omissions, and they do not effectively manage postprandial glucose excursions or exercise-induced hypoglycemia.
Innovation Solution
A method that eliminates the need for meal and exercise announcements by incorporating an add-on module with an internal model control (IMC) loop, which generates an automatic insulin correction signal and suggests rescue carbohydrates for hypoglycemia mitigation, using a switching logic to convert virtual insulin signals into insulin infusion, rescue carbohydrate suggestions, and insulin-on-board reduction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If patient-initiated meal boluses are used in hybrid artificial pancreas systems, then postprandial glucose excursions are reduced, but the system requires timely and accurate patient intervention which is challenging and leads to estimation errors, bolusing delays, or omissions
Solution Approach 1:
The system automatically detects meal events and generates insulin boluses without requiring patient initiation. The meal detector monitors glucose data patterns to identify meals autonomously, and the controller automatically calculates and delivers the appropriate insulin bolus, eliminating the need for patient intervention in meal announcement
Solution Approach 2:
The system continuously monitors glucose data and uses feedback from glucose trends to detect meal events. The controller adjusts insulin delivery based on real-time glucose measurements and detected meal patterns, creating a closed-loop system that responds to physiological changes without patient input
2Reliability
If hybrid artificial pancreas systems modify glucose reference or basal profile to reduce exercise impact, then exercise-induced glucose unbalance is mitigated, but subjects must announce exercise time or intensity even with anticipation
Solution Approach 1:
The system automatically detects exercise events by analyzing glucose data patterns and physiological responses. The controller autonomously identifies exercise onset and duration, and automatically adjusts insulin delivery and glucose reference without requiring patient announcement of exercise activities
Solution Approach 2:
The system proactively adjusts insulin delivery and glucose reference before hypoglycemia occurs during exercise. By detecting early exercise indicators in glucose trends, the controller preemptively modifies insulin dosing and raises glucose reference to prevent glucose drops, rather than waiting for patient announcement or severe hypoglycemia
3Ease of operation
If fully automated meal detection and insulin bolus generation is implemented, then user intervention is eliminated, but the system complexity increases
Solution Approach 1:
The automated system is divided into separate functional modules: a meal detector module that analyzes glucose patterns to identify meals, a controller module that calculates insulin boluses, and an executor module that delivers insulin. This segmentation allows each module to perform a specific function independently, managing complexity through modular design
Data Source
AI summary
A method for improving blood glucose control of a hybrid controller meal and exercise announcement by substituting patient-initiated meal boluses of said hybrid controller by an automatic insulin correction signal without retuning of said hybrid controller, and by incorporating rescue carbohydrates suggestion for hypoglycemia mitigation, comprising the steps of measuring a plasma glucose (G(t)) signal by means of a continuous glucose monitor (CGM), calculating a glucose level (Ĝ(t)) by using a glucose-insulin model; computing a disturbance term d(t), generating a virtual signal uIMC(t), for mitigating the effect of d(t) on the output, by means of an IMC filter Q(s) and converting the virtual signal uIMC(t) into three feed forward actions: insulin infusion, rescue carbohydrate suggestion, and insulin-on-board reduction.