Personalized Calibration Model for Glucose Sensor Accuracy
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Solution Overview
Problem
Current insulin infusion pump systems face challenges in accurately regulating blood glucose levels due to variations in individual insulin response and daily activities, as well as limitations in continuous glucose monitoring devices, leading to uncertainties and inaccuracies in glucose control.
Innovation Solution
The implementation of personalized, patient-specific parameter models that use current operational context information to calculate calibration factors for converting electrical signals into calibrated measurement values, allowing for dynamic adjustments in control schemes and improved glucose management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If continuous glucose monitoring is used to regulate blood glucose levels, then glucose regulation can be performed in a substantially continuous and autonomous manner, but variations in individual insulin response and daily activities as well as device limitations lead to uncertainties and inaccuracies in glucose control
Solution Approach 1:
The system dynamically changes calibration factors based on operational context parameters such as sensor location, time of day, and patient activity level. By adjusting the calibration factor according to these varying parameters, the system maintains measurement accuracy despite changes in individual insulin response and daily activities, directly addressing the contradiction between autonomous operation and measurement precision.
2Ease of operation
If standardized calibration factors are used for glucose monitoring, then device operation is simplified, but individual variations in insulin response and operational context reduce the accuracy of glucose measurements
Solution Approach 1:
The system transitions from static, standardized calibration factors to dynamic, context-dependent calibration factors. The calibration factor is automatically adjusted based on real-time operational context including sensor location, time, and patient state, maintaining ease of operation while significantly improving measurement precision for individual patients.
Solution Approach 2:
The system changes the calibration parameter based on operational context variables. By implementing context-dependent calibration factors that adapt to individual patient variations and environmental conditions, the system resolves the contradiction between simplified operation and accurate measurement.
3Measurement precision
If calibration factors are adjusted to account for individual variations and operational context, then glucose measurement accuracy is improved, but the complexity of the control system increases
Solution Approach 1:
The system performs self-calibration by automatically determining context-dependent calibration factors without requiring manual intervention. The device monitors its own operational context (sensor location, time, activity level) and autonomously adjusts calibration parameters, improving measurement accuracy while minimizing the increase in perceived system complexity for the user.
Solution Approach 2:
The system implements feedback mechanisms where glucose measurements and operational context data are continuously monitored and used to adjust calibration factors. This closed-loop approach improves measurement precision while managing system complexity through automated feedback-based parameter adjustment.
Data Source
AI summary
A processor-implemented method comprises obtaining current operational context information associated with a sensing device; obtaining an expected calibration factor parameter model associated with a patient; calculating an expected calibration factor value based on the expected calibration factor parameter model and the current operational context information; obtaining one or more electrical signals from the sensing device, the one or more electrical signals having a signal characteristic indicative of a physiological condition; converting the one or more electrical signals into a calibrated measurement value for the physiological condition using the expected calibration factor value; and outputting the calibrated measurement value for the physiological condition.