Cloud Big Data Blood Glucose Monitoring Signal Correction
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Solution Overview
Problem
Amperometric glucose sensors face challenges in accuracy due to individual chemical differences, lifestyle habits, and physical conditions, leading to signal interference and the need for manual recalibration, which increases user inconvenience and health risks.
Innovation Solution
An intelligent real-time dynamic blood glucose monitoring system using a cloud-based big data system with an implantable sensor, smartphone app, and finger blood glucose meter, which employs electrochemical impedance spectrum measurement and regression algorithms to automatically correct sensor data, ensuring accurate glucose monitoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual recalibration is performed to correct sensor signal interference, then measurement accuracy is improved, but device complexity and user inconvenience increase
Solution Approach 1:
The system automatically performs recalibration and signal correction without requiring user intervention. The cloud server uses historical data and regression algorithms to automatically adjust sensor signals, making the system self-calibrating and eliminating the need for manual user operations to maintain accuracy.
Solution Approach 2:
The system continuously feeds back historical glucose measurement data from the cloud server to automatically adjust and correct sensor signals in real-time. This feedback loop enables the system to learn from past measurements and automatically compensate for signal interference patterns.
2Measurement precision
If manual recalibration is performed to correct sensor signal interference, then measurement accuracy is improved, but ease of operation deteriorates
Solution Approach 1:
The system automatically performs recalibration and signal correction without requiring user intervention. The cloud server uses historical data and regression algorithms to automatically adjust sensor signals, making the system self-calibrating and eliminating the need for manual user operations to maintain accuracy.
Solution Approach 2:
The cloud server acts as an intermediary that handles the complex recalibration calculations automatically. Instead of requiring users to perform manual recalibration operations, the cloud server mediates between the sensor data and the final glucose readings, automatically adjusting for signal interference.
3Measurement precision
If manual recalibration is performed to correct sensor signal interference, then measurement accuracy is improved, but health risk increases
Solution Approach 1:
The system automatically performs recalibration and signal correction without requiring user intervention. The cloud server uses historical data and regression algorithms to automatically adjust sensor signals, making the system self-calibrating and eliminating the need for manual user operations to maintain accuracy.
Solution Approach 2:
The system continuously feeds back historical glucose measurement data from the cloud server to automatically adjust and correct sensor signals in real-time. This feedback loop enables the system to learn from past measurements and automatically compensate for signal interference patterns.
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
The system effectively corrects signal interference, improving the accuracy and reliability of glucose monitoring by using historical data to adjust sensor output, reducing user inconvenience and health risks.
Implementation Method 1
a signal transmitter with an electrochemical impedance spectrum measurement function, wherein the output of electrochemical impedance spectrum measurement includes the impedance value and phase of the impedance or the real and imaginary parts of the impedance
Data Source
AI summary
Intelligent real-time blood glucose monitoring system and method based on cloud big data is disclosed. The system includes an implantable dynamic glucose sensor, a smart phone, a blood glucose monitoring software application installed on the smart phone, a finger blood glucose meter, and a big data cloud server. By processing historical blood glucose measurement data of a user stored in the cloud, the monitoring system effectively corrects and influences signal differences produced by individual users so as to ensure the validity and accuracy of measurement signals during sensor operation.