Multivariable Artificial Pancreas Recursive Model Glucose Control
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Solution Overview
Problem
Current artificial pancreas systems for Type 1 diabetes management struggle to accurately predict and regulate blood glucose levels without manual inputs, particularly in response to varying factors like meals, physical activity, and acute stress, and lack effective error detection and adaptive control mechanisms.
Innovation Solution
A closed-loop system that includes a continuous glucose monitor, physiological status monitoring, and an automatic controller using recursive multivariate time-series models to predict glucose levels and adjust insulin infusion based on physiological states like physical activity, stress, and sleep, while also detecting equipment errors and malfunctions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual inputs (meal and exercise information) are required for glucose regulation, then control accuracy improves, but patient convenience and adherence deteriorate
Solution Approach 1:
The system automatically detects meals and exercise activities without requiring patient input. Sensors monitor physiological parameters (glucose levels, activity markers) and the control system autonomously identifies meal events and exercise sessions, allowing the system to serve itself by eliminating the need for manual patient reporting while maintaining accurate glucose regulation
Solution Approach 2:
Manual patient reporting of meal and exercise information is replaced with automated sensor-based detection. The system uses physiological sensors and algorithms to detect meal events through glucose pattern recognition and exercise through activity level monitoring, substituting the mechanical action of manual input with automated electronic detection
2Productivity
If automated insulin infusion control is implemented, then glucose regulation improves, but risk of hypoglycemia increases
Solution Approach 1:
The system performs preliminary detection of meal events and exercise activities before they significantly impact glucose levels. By identifying meal events through glucose pattern recognition and predicting exercise-induced glucose changes in advance, the system can proactively adjust insulin infusion rates to prevent hypoglycemia before it occurs
Solution Approach 2:
The system continuously monitors glucose levels and uses this feedback to dynamically adjust insulin infusion rates. The closed-loop control system processes real-time glucose data, detects trends, and modifies insulin delivery to maintain glucose within target ranges, automatically reducing infusion rates when hypoglycemia is predicted based on glucose trajectory and detected physiological states
3Measurement precision
If recursive multivariate time-series models are used for glucose prediction, then prediction accuracy improves, but system complexity increases
Solution Approach 1:
The recursive multivariate time-series model serves multiple functions simultaneously: it predicts future glucose levels, detects meal events through pattern recognition, identifies exercise activities, and provides feedback for control decisions. This single multi-functional model reduces overall system complexity compared to having separate dedicated systems for each function
Solution Approach 2:
The system combines multiple detection functions (meal detection, exercise detection, glucose prediction) into a unified recursive time-series modeling framework. By merging these functions into a single integrated model that processes glucose data and physiological parameters together, the system achieves high prediction accuracy while managing complexity through functional integration
Data Source
AI summary
Methods and modules for using physiological (biometric) variables to advance the state of the artificial pancreas. The method and system includes one or more modules for recursive model identification, hypoglycemia early alert and alarm, adaptive control, hyperglycemia early alert and alarm, plasma insulin concentration estimation, assessment of physical activity (e.g., presence, type, duration, expected effects on insulin sensitivity and GC), detection of acute stress and assessment of its impact on insulin sensitivity, detection of sleep and its stages and assessment of sleep stages on GC, sensor fault detection and diagnosis, software and controller performance evaluation and adjustment and/or pump fault detection and diagnosis.