Metabolism Model Corrects Bio-Sensor Glucose Errors
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Solution Overview
Problem
Current bio-sensors for monitoring blood glucose levels in diabetes management face inaccuracies due to individual variations in glycemic response, necessitating personalized correction models to improve measurement accuracy.
Innovation Solution
An apparatus and method that generate a metabolism model using a processor to obtain and analyze bio-information profiles from a bio-sensor, extracting a representative profile to correct errors and provide guide information for users, incorporating techniques like mean value, median value, filtering, and machine learning to account for individual physiological characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Duration of action of moving object
If a bio-sensor is used to monitor blood glucose levels, then continuous monitoring is enabled, but measurement accuracy deteriorates due to individual variations in glycemic response
Solution Approach 1:
The system performs preliminary actions by collecting multiple bio-information profiles over time and generating a personalized metabolism model before actual glucose monitoring. This pre-characterization of individual glycemic response enables the system to compensate for measurement inaccuracies during continuous monitoring, resolving the contradiction between continuous monitoring capability and measurement accuracy.
Solution Approach 2:
The system implements feedback by using the generated metabolism model to continuously correct and refine glucose level estimates based on individual response patterns. The model learns from historical bio-information profiles and adjusts measurements in real-time, maintaining accuracy throughout continuous monitoring operations.
2Measurement precision
If multiple bio-information profiles are collected to improve accuracy, then measurement precision improves, but loss of time increases due to repeated measurements
Solution Approach 1:
The system performs the time-consuming data collection and model generation as a preliminary action during an initial setup phase. Once the personalized metabolism model is established, accurate glucose monitoring can be performed continuously without requiring repeated profile collections, thus improving precision without ongoing time loss.
Solution Approach 2:
The system creates a simplified metabolic response model that copies and represents the complex individual glycemic response patterns. This model can then be applied repeatedly to estimate glucose levels without requiring repeated collection of full bio-information profiles, reducing time loss while maintaining precision.
3Measurement precision
If a personalized metabolism model is generated, then measurement precision improves by correcting sensor errors, but device complexity increases
Solution Approach 1:
The system introduces a metabolism model as an intermediary computational layer between the raw bio-sensor data and the final glucose measurement. This model acts as a mediator that translates imperfect sensor readings into accurate glucose estimates by compensating for individual variations, improving precision while keeping the complexity contained within a software-based processing layer.
Data Source
AI summary
An apparatus for generating a metabolism model may include a processor configured to obtain a predetermined number of bio-information profiles from a bio-sensor, extract a representative bio-information profile from the obtained predetermined number of bio-information profiles, and generate the metabolism model for correcting an error of the bio-sensor by using the extracted representative bio-information profile.