Pulse Wave Bio-Information Estimation via Parameter Segmentation
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Solution Overview
Problem
Current bio-information estimation technologies, particularly for mobile healthcare, face challenges in accurately monitoring cardiovascular health conditions outside traditional medical settings due to limitations in measuring and analyzing pulse wave signals effectively.
Innovation Solution
A bio-information estimating apparatus and method that includes a pulse wave sensor to measure pulse wave signals and a processor to extract parameters such as the maximum, average, and onset points from the waveform, generating analysis results to estimate bio-information like blood pressure, vascular compliance, and cardiac output, while preprocessing the signals to remove noise and calibrate data for accurate readings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pulse wave signals are measured and analyzed using conventional methods, then bio-information can be estimated, but the measurement precision and reliability are insufficient for accurate cardiovascular health monitoring
Solution Approach 1:
The pulse wave signal analysis is segmented into multiple distinct parameters: maximum point, average point, and onset point. Each parameter is extracted and analyzed separately to provide comprehensive cardiovascular health information, improving both measurement precision and reliability of bio-information estimation.
Solution Approach 2:
The processor performs preliminary processing of the pulse wave signal to identify and extract key parameters (maximum point, average point, onset point) before generating the final analysis result. This preliminary extraction of critical features enhances the accuracy and reliability of subsequent bio-information estimation.
2Measurement precision
If multiple parameters are extracted from pulse wave waveform, then bio-information estimation accuracy improves, but the device complexity increases
Solution Approach 1:
The complex task of pulse wave analysis is segmented into extracting three specific parameters (maximum point, average point, onset point). This segmentation simplifies the processing complexity while maintaining high estimation accuracy, as each parameter can be independently calculated from the waveform.
Solution Approach 2:
The system transforms the continuous pulse wave signal into discrete parameter values (maximum point, average point, onset point). This parameter transformation simplifies the data structure and reduces processing complexity while preserving the essential information needed for accurate bio-information estimation.
3Measurement precision
If noise is removed from pulse wave signal through preprocessing, then measurement precision improves, but the processing time increases
Solution Approach 1:
Noise removal and signal preprocessing are performed as preliminary actions before parameter extraction. By cleaning the signal first, the subsequent parameter identification (maximum point, average point, onset point) becomes more accurate and can be performed more efficiently, ultimately reducing total processing time.
Solution Approach 2:
The preprocessing transforms the noisy pulse wave signal into a cleaner signal with more distinct features. This parameter change in signal quality makes the subsequent parameter extraction faster and more accurate, as the key features (maximum point, average point, onset point) become more easily identifiable.
Data Source
AI summary
An apparatus for estimating bio-information, includes a pulse wave sensor configured to measure a pulse wave signal from a user, and a processor configured to obtain two or more parameters among a maximum point, an average point, and an onset point, from a waveform of the measured pulse wave signal, and combine the obtained two or more parameters to generate a pulse wave analysis result.