Spectral Glucose Testing With Cloud-Personalized Model Updating
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Solution Overview
Problem
Existing methods for non-invasive blood glucose testing face challenges due to individual differences in spectral data characteristics, leading to inaccurate results, and existing systems are bulky, expensive, or require complex algorithms with multiple biological signals, making real-time testing difficult.
Innovation Solution
A method and system that utilizes spectral data analysis involving local and cloud testing models, with infrared and ultraviolet light to distinguish between venous and non-venous blood vessels, and fluorescence spectroscopy to obtain accurate glucose concentration without invasive methods, using a cloud platform for model training and updating.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If a unified testing model is used for all people, then the system is simple to operate, but the measurement precision deteriorates due to individual differences in spectral data characteristics
Solution Approach 1:
The system transitions from a static unified model to a dynamic personalized model that adapts to each user's spectral characteristics. The model automatically learns individual differences through iterative training on user-specific spectral data, enabling accurate measurements for diverse individuals while maintaining ease of use through automated adaptation
Solution Approach 2:
The system performs self-optimization by automatically training personalized models using each user's own spectral data without requiring manual intervention. The model self-adjusts to capture individual characteristics through iterative learning processes, achieving high precision while requiring minimal user input
2Measurement precision
If laboratory-grade Raman spectroscopy system is used, then the measurement precision is improved, but the device complexity and cost increase significantly
Solution Approach 1:
The patent replaces expensive laboratory-grade Raman spectroscopy systems with a cost-effective handheld device using fluorescence spectroscopy. The system uses inexpensive optical components and disposable fluorescent probes to achieve accurate blood glucose measurements, making the technology accessible for portable use
Solution Approach 2:
The system substitutes complex mechanical Raman spectroscopy equipment with a simplified fluorescence-based optical measurement system. By replacing the mechanical complexity of Raman systems with a more straightforward fluorescence detection approach, the patent achieves comparable measurement precision with significantly reduced device complexity
3Measurement precision
If multiple biological signals are collected from different body positions, then the measurement precision may be improved, but the device complexity and cost increase excessively
Solution Approach 1:
The patent extracts and focuses on a single critical measurement location (venous blood vessels in the imaging area) rather than collecting multiple biological signals from different body positions. By concentrating the measurement on the most informative region where glucose concentration is directly relevant, the system achieves high precision without requiring multiple sensors or complex multi-position measurement capabilities
4Device complexity
If spectral signals from different wavelengths are mixed together, then the device complexity is reduced, but the measurement precision deteriorates due to difficulty in separating spectral signals
Solution Approach 1:
The system segments the spectral measurement into distinct wavelength ranges: a first wavelength range for collecting fluorescence emission signals and a second wavelength range for collecting excitation light signals. This segmentation allows the system to separately characterize the analyte's spectral signature from the excitation source, enabling accurate glucose measurement while maintaining relatively simple device architecture
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate, non-invasive, and real-time blood glucose testing with reduced costs and system miniaturization by utilizing fluorescence spectroscopy and model updating, improving testing accuracy and convenience.
Implementation Method 1
irradiating, by light within a second wavelength range, a same position in the imaging area, to obtain a second image of the imaging area, where the second image includes spectral data in the imaging area that indicate a fluorescence radiation signal generated by the analyte when irradiated by light
Implementation Method 2
the first image includes distribution data that indicate distribution in an imaging area of a reflection signal or an excitation signal generated by the analyte when irradiated by light
Data Source
AI summary
The present invention provides a method and a system for using spectral data of an analyte, a method and a system for testing an analyte, a medium, and a device, which relate to the field of optical analysis. The method includes: analyzing step: inputting obtained spectral data into a local testing model to obtain information about the analyte, and providing a correction option; information uploading step: after the correction option is triggered, obtaining correction information input by a user, and uploading the correction information and the obtained spectral data to a cloud platform; model training step: using, by the cloud platform, the obtained spectral data as an input and the obtained correction information as an output to train a cloud testing model; and model updating step: updating the local testing model based on a trained cloud testing model.