Glucose Sensor Data Processing with Probability Analysis and Recursive Filtering
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Solution Overview
Problem
Continuous glucose monitoring systems face challenges in accurately measuring glucose levels due to sensor noise and malfunctions, which can lead to deviations from actual glucose levels, especially due to physical movement and electrical issues, resulting in inaccurate readings.
Innovation Solution
A method using a probability analysis tool and a recursive filter to estimate glucose levels by analyzing measured glucose results, determining the probability of sensor accuracy, and weighting the results to filter out noise and malfunctions, thereby providing an accurate estimation and prediction of glucose levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If a glucose sensor is used to continuously monitor glucose levels, then real-time glucose monitoring is achieved, but sensor noise and malfunctions cause deviations from actual glucose levels
Solution Approach 1:
The patent introduces an intermediary processing system that includes a probability analysis tool and recursive filter. This intermediary layer processes the raw sensor data, evaluates the likelihood of sensor accuracy, and filters out noisy or malfunctioning readings before presenting the estimated glucose level to the user, thus resolving the contradiction between real-time monitoring and measurement precision
Solution Approach 2:
The system implements feedback by continuously analyzing measured glucose results to determine the probability of sensor accuracy, then using this probability information to weight and filter subsequent measurements. This closed-loop feedback mechanism allows the system to adapt to sensor performance variations and maintain accuracy despite noise and malfunctions
2Measurement precision
If sensor data is processed with filtering algorithms, then measurement accuracy is improved, but processing time and computational complexity increase
Solution Approach 1:
The patent applies partial action by using a recursive filter that processes only the necessary portion of the data stream with appropriate weighting based on sensor accuracy probability. Rather than applying heavy filtering to all data equally, the system selectively processes data based on its reliability, reducing unnecessary computational overhead while maintaining accuracy for critical measurements
Solution Approach 2:
The system dynamically changes the weighting parameter in the recursive filter based on the determined probability of sensor accuracy. When sensor accuracy is high, more weight is given to recent measurements; when accuracy is low, less weight is applied. This dynamic parameter adjustment optimizes the balance between processing speed and accuracy
Data Source
AI summary
Methods and systems are disclosed for estimating a glucose level of a person having diabetes comprises. One method may comprise: receiving into a computing device a plurality of measured glucose results from a glucose sensor coupled to the person; using the computing device to analyze the plurality of measured glucose results with a probability analysis tool configured to determine a probability of glucose sensor accuracy based on the plurality of measured glucose results; and using the computing device to estimate a glucose level of the person with a recursive filter configured to estimate the glucose level based on the plurality of measured glucose results weighted with the probability of glucose sensor accuracy.