Automated Blood Glucose Control With Activity-Aware Physiological Modeling
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Solution Overview
Problem
Existing automated blood glucose regulation systems face reliability issues in predicting future trends due to the lack of consideration for the patient's physical activity, leading to potential errors in glucose control and increased health risks.
Innovation Solution
Incorporating a physiological model that takes into account the patient's physical activity, measured by motion and heart rate sensors, to calculate energy expenditure, and adjusts insulin dosage predictions accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a physiological model is used to predict future blood glucose trend, then automated blood glucose control can be implemented, but the prediction reliability deteriorates when physical activity is not considered
Solution Approach 1:
The physiological model is enhanced by dynamically incorporating physical activity data from motion sensors and heart rate monitors. The model adapts its predictions based on real-time activity levels, transitioning from a static model to one that responds dynamically to changing patient conditions, thereby maintaining reliability across various activity states
Solution Approach 2:
The model incorporates additional parameters related to physical activity (motion data, heart rate) alongside traditional glucose and insulin parameters. By changing the parameter set to include activity-related variables, the model achieves both reliable predictions and adaptability to different physical activity conditions
2Reliability
If physical activity sensors are added to measure energy expenditure, then prediction reliability during activity improves, but device complexity increases
Solution Approach 1:
The processing and control unit serves multiple functions: it processes glucose sensor data, insulin pump data, and now physical activity data from motion and heart rate sensors. By making the control unit multi-functional, the system integrates activity monitoring without requiring separate dedicated processing hardware, thus improving reliability while limiting complexity growth
Solution Approach 2:
The system uses the patient's own physiological signals (heart rate, motion) to automatically determine energy expenditure and adjust glucose predictions. The physical activity sensors leverage the patient's body as the measurement medium, eliminating the need for external bulky equipment or complex manual input mechanisms
Data Source
AI summary
An automated system for controlling a patient's blood glucose, including a processing and control unit configured to predict the future trend of the patient's blood glucose based on a physiological model, wherein the physiological model includes a differential equation system describing the time variation of a plurality of state variables, and wherein at least one of the equations of the system takes as an input a variable EE(t) representative of the time variation of the patient's energy expenditure.