Physiological Data Pattern Recognition for Glucose Monitoring
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Solution Overview
Problem
Current methods for continuous glucose monitoring are cumbersome and time-consuming, particularly for patients with diabetes, as they require manual data processing and pattern recognition, which can be subjective and inefficient, especially for large data sets, and lack effective solutions for processing identified patterns.
Innovation Solution
A method and device for analyzing physiological measurement values that automatically identifies historical situations similar to the current situation using pattern recognition, allowing for real-time data analysis and reducing data volume through data reduction techniques, enabling users to react promptly to current conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual data processing and pattern recognition methods are used for continuous glucose monitoring, then users can analyze physiological data, but the process becomes cumbersome and time-consuming
Solution Approach 1:
The system performs automatic pattern recognition and data analysis without requiring manual user intervention. The processor automatically identifies patterns in glucose data, compares them against stored patterns, and generates alerts, enabling the system to serve itself rather than requiring continuous manual processing by the user.
Solution Approach 2:
The patent replaces manual mechanical data processing with automated electronic processing. The processor uses algorithmic pattern recognition instead of manual analysis, substituting electronic automation for human manual operations to dramatically improve processing speed and efficiency.
2Measurement precision
If large data sets from continuous monitoring are analyzed manually, then comprehensive pattern recognition is possible, but the process becomes subjective and inefficient
Solution Approach 1:
The system automatically performs pattern recognition and analysis without requiring manual user intervention. The processor independently identifies patterns, compares them against stored reference patterns, and generates alerts, eliminating the need for users to manually analyze large data sets and remove subjectivity from the process.
Solution Approach 2:
The system incorporates feedback mechanisms where pattern recognition results are automatically processed and used to generate alerts or notifications. The system continuously monitors glucose data, compares it against stored patterns, and provides feedback through alerts when significant patterns are detected, creating a closed-loop automated analysis system.
3Speed
If real-time data analysis is implemented, then users can respond promptly to current conditions, but processing time and computational requirements increase
Solution Approach 1:
The system performs preliminary actions by pre-storing reference patterns and processing algorithms in memory before real-time analysis is needed. During real-time operation, the processor simply compares incoming glucose data against these pre-prepared patterns, significantly reducing the computational complexity required for immediate real-time response while maintaining fast processing speed.
Data Source
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AI summary
A method for analysing physiological measurement values of a user is proposed, in particular for identifying the presence of a physiological body state of the user. The method comprises: a) at least one data acquisition step, wherein, during the data acquisition step, physiological measurement values of the user are acquired at different measurement times and stored in a measurement data record; b) at least one pattern selection step, wherein,during the pattern selection step, measurement values acquired during one comparison time interval are selected as at least one comparison pattern; c) at least one pattern recognition step, wherein, during the pattern recognition step, patterns corresponding to the comparison pattern are sought after in the measurement data record. Further, a corresponding computer program and a device for analysing physiological measurement values of a user is provided by the invention.