Biometric Identity Confirmation Using Pulse Wave and Spirometric Data
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Solution Overview
Problem
Current biometric identification methods face challenges in specificity, constancy over time, and ease of counterfeiting, particularly with single-parameter biometrics like weight, and require sophisticated algorithms for image processing in techniques like fingerprint and facial scans.
Innovation Solution
A system utilizing both pulse wave shape and spirometric data for biometric identity confirmation, generating subject characterization data during enrollment and analyzing it during authentication to verify identity, with dynamic and feature weighting to enhance distinguishability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single parameter biometric identifiers (such as weight or height) are used, then the system is simple to implement, but the specificity and reliability of identification deteriorates
Solution Approach 1:
The patent combines multiple biometric parameters (pulse wave characteristics including systolic upstroke, dicrotic notch, and respiratory cycle phases) into a unified identification system. This merging of parameters maintains relative system simplicity while dramatically improving identification reliability by creating a multi-dimensional biometric profile that is difficult to counterfeit.
Solution Approach 2:
The system creates a composite biometric identifier by integrating temporal, spectral, and morphological features of pulse waves and respiratory cycles. This composite approach synthesizes multiple physiological signals into a single robust identification metric that overcomes the limitations of single-parameter systems.
2Reliability
If image processing techniques (fingerprint, retinal, iris, facial scans) are used, then the specificity of identification improves, but the computational complexity and processing requirements worsen
Solution Approach 1:
The patent replaces complex image processing algorithms with temporal signal analysis of pulse waves and respiratory cycles. Instead of analyzing two-dimensional images requiring sophisticated computer vision algorithms, the system processes one-dimensional time-series physiological signals using simpler spectral and temporal analysis methods while maintaining high identification specificity.
Solution Approach 2:
The system segments the physiological signal into distinct analyzable components including systolic upstroke, dicrotic notch, and respiratory cycle phases. This segmentation breaks down the complex signal into manageable features that can be analyzed independently and combined for identification, reducing overall computational complexity.
3Ease of operation
If traditional biometric methods are used, then identification can be performed, but the vulnerability to counterfeiting and metabolic fluctuations worsens
Solution Approach 1:
The system captures dynamic physiological patterns that change with metabolic state rather than static anatomical features. By analyzing the temporal evolution of pulse wave morphology and respiratory cycle characteristics, the system creates a living biometric profile that adapts to metabolic fluctuations while remaining resistant to counterfeiting, as these dynamic patterns are difficult to replicate.
Data Source
AI summary
A biometric identity confirmation system is based on both pulse wave shape data and spirometric data for the subject. During an initial enrollment mode, pulse wave shape and spirometric data for a known subject are used to generate subject characterization data for the known subject by computing an exemplar and selectively weighting portions of the exemplar based, for example, on repeatability or distinguishing characteristic features over the population of known subjects. During a subsequent identity authentication mode, pulse wave shape and spirometric data for a test subject are analyzed using the subject characterization data to confirm whether the identity of the test subject matches the known subject.