Probability-Based Controller Gain for Glucose Sensor Noise
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Solution Overview
Problem
Continuous glucose monitoring systems face challenges due to sensor noise and malfunctions, which can lead to inaccurate glucose level readings, affecting insulin delivery and glucose control in diabetes management.
Innovation Solution
A controller for insulin delivery that implements probability-based controller gain, using a calculated quality of signal input from a glucose sensor to determine the appropriate insulin delivery method, either open-loop or closed-loop control, based on a total quality score derived from the sensor's accuracy and impedance values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If continuous glucose monitoring is implemented to maintain real-time glucose control, then glucose control effectiveness is improved, but measurement precision deteriorates due to sensor noise and malfunctions
Solution Approach 1:
The system implements feedback by continuously monitoring glucose sensor readings and using a probability analysis tool to assess data quality. The controller receives feedback about sensor accuracy probability and adjusts insulin delivery accordingly, switching between closed-loop and open-loop control based on the reliability of sensor data.
Solution Approach 2:
The system dynamically adjusts the controller gain based on the probability of sensor accuracy. When sensor accuracy is high, the system uses higher gain for aggressive glucose control. When accuracy drops below thresholds, the system reduces gain and switches to open-loop control, making the control strategy adaptive to changing sensor reliability conditions.
2Measurement precision
If closed-loop control is used for automatic insulin delivery, then insulin delivery accuracy is improved, but system reliability deteriorates when sensor data quality is poor
Solution Approach 1:
The control mode dynamically switches between closed-loop and open-loop based on sensor accuracy probability. The system maintains closed-loop control when sensor data is reliable, achieving accurate automatic insulin delivery. When sensor reliability drops below a threshold, the system transitions to open-loop control, preventing unreliable automated decisions while maintaining system operation.
Solution Approach 2:
The probability analysis tool acts as an intermediary between the glucose sensor and the insulin pump controller. It assesses the quality of sensor data and provides a probability metric that mediates the decision-making process, determining whether closed-loop or open-loop control should be used based on data reliability.
3Device complexity
If sensor data is used without quality assessment, then device complexity is reduced, but control precision deteriorates due to noisy or malfunctioning sensor readings
Solution Approach 1:
The probability analysis tool serves as a relatively simple intermediary that assesses sensor data quality without adding significant complexity to the system. It analyzes sensor readings and provides a probability metric that the controller uses to adjust its behavior, improving precision while maintaining manageable system complexity.
Solution Approach 2:
The system changes the controller gain parameter based on sensor data quality assessment. When sensor accuracy is high, the controller uses higher gain for precise control. When accuracy drops, the system reduces gain and switches control modes, allowing the controller to adapt its precision to the quality of available sensor data.
Data Source
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AI summary
Methods and systems are disclosed for estimating a glucose level of a person having diabetes and selecting automatically open-loop and closed-loop control for a connected therapy delivery device. The method may comprise analyzing measured glucose results and corresponding impedance values received from a glucose sensor coupled to the person with a probability analysis tool implemented by a microcontroller to determine a total quality score that is based on the minimum constraint of a probability of glucose sensor accuracy determined measured glucose results and a probability of sensing quality determined from the impedance values. The microcontroller may estimate the glucose level of the person with a recursive filter based on the plurality of measured glucose results weighted with the total quality score and select automatically either open-loop control or closed-loop control for the connected therapy delivery device based on the value of the total quality score.