Physiological Data Pattern Recognition for Continuous Monitoring
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Solution Overview
Problem
Current methods for analyzing physiological measurement data, particularly in continuous glucose monitoring, are cumbersome and inefficient, especially for users like children or those with dementia, as they require manual and time-consuming processes for pattern recognition and similarity quantification, leading to difficulties in identifying suitable historical situations for current conditions.
Innovation Solution
A method and device for analyzing physiological measurement values that automatically and efficiently identify historical situations similar to current ones by acquiring and processing data, using pattern recognition techniques to generate a reduced data record and match current patterns with historical data, allowing for real-time and online analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual pattern recognition methods are used for analyzing physiological measurement data, then users can identify historical situations similar to current conditions, but the process becomes time-consuming and cumbersome
Solution Approach 1:
The patent replaces manual mechanical pattern recognition with automated computational algorithms. The system automatically compares current physiological data patterns against historical data using computer-based pattern recognition techniques, eliminating the need for manual analysis while maintaining or improving recognition accuracy.
Solution Approach 2:
The system enables self-service by automatically performing pattern recognition and historical situation identification without requiring user intervention. The automated algorithm independently processes physiological measurement data, identifies patterns, and retrieves relevant historical situations, making the system usable even by individuals with cognitive impairments.
2Measurement precision
If comprehensive physiological measurement data is collected for accurate pattern matching, then pattern recognition accuracy improves, but data complexity and processing requirements increase
Solution Approach 1:
The patent extracts and focuses on the most relevant features and characteristics from comprehensive physiological measurement data. Rather than processing all raw data equally, the system identifies and extracts key pattern-defining parameters, reducing processing complexity while maintaining pattern matching accuracy.
Solution Approach 2:
The system segments physiological measurement data into meaningful patterns and time windows. By dividing continuous data streams into discrete, analyzable segments with specific characteristics, the system manages data complexity while preserving the information needed for accurate pattern recognition.
3Adaptability or versatility
If manual pattern recognition processes are used, then users can analyze physiological data, but the system becomes difficult to operate for users with cognitive impairments
Solution Approach 1:
The patent replaces manual cognitive operations with automated computational processes. The system handles all complex pattern recognition and data analysis tasks through automated algorithms, making the interface simple and accessible for users with cognitive impairments while maintaining comprehensive analytical capabilities.
Solution Approach 2:
The system performs self-service by automatically executing the entire pattern recognition workflow without requiring user cognitive engagement. Users simply provide physiological data, and the system independently completes pattern identification, historical matching, and result presentation, accommodating users with various cognitive abilities.
Data Source
AI summary
A method for analysing physiological measurement values of a user is proposed. The method comprises 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; 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; and 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.