Biometric Identification Using Pulse Wave Segmentation and Noise Filtering
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Solution Overview
Problem
Existing biometric identification devices based on pulse wave information face challenges in accurately identifying biological subjects due to variations in pulse waves between individuals and the influence of noise in the pulse wave data.
Innovation Solution
An identification device and method that generate factor information related to pulse-wave information using biological model information, and select appropriate list information based on predetermined criteria to accurately identify biological subjects, incorporating noise filtering and pressure control procedures to enhance accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If pulse wave information is used for biometric identification, then identification capability is provided, but measurement precision deteriorates due to noise and individual variations
Solution Approach 1:
The pulse wave information is segmented into multiple characteristic points (first characteristic point and second characteristic point) with different physiological meanings. By dividing the pulse wave analysis into distinct segments, the system can extract multiple independent features for identification, reducing the impact of noise on any single measurement and improving overall identification reliability.
Solution Approach 2:
The system transforms the raw pulse wave signal into multiple derived parameters including time differences between characteristic points, amplitude ratios, and other statistical features. This parameter transformation converts noisy raw signals into more stable identification features that are less sensitive to measurement variations and environmental noise.
2Reliability
If acceleration pulse wave is used for identification, then identification function is achieved, but measurement precision worsens due to noise sensitivity
Solution Approach 1:
The system introduces an intermediary processing stage that converts acceleration pulse wave data into multiple characteristic point measurements and derived parameters. This intermediary transformation layer filters out direct noise effects by extracting physiological features that are inherently more stable, thereby maintaining identification reliability while reducing noise sensitivity.
Solution Approach 2:
The system transitions from analyzing acceleration pulse wave in a single dimension to examining multiple dimensions including time differences, amplitude relationships, and characteristic point patterns. This multi-dimensional analysis provides redundant information paths that compensate for noise in any single measurement, improving overall precision.
3Device complexity
If simple pulse wave analysis is used, then device complexity is reduced, but identification accuracy deteriorates
Solution Approach 1:
The analysis method segments pulse wave data into multiple characteristic points and computes multiple derived parameters from these segments. This segmentation approach enables comprehensive feature extraction without requiring complex hardware, achieving high identification accuracy through sophisticated software-based analysis of divided signal components.
Solution Approach 2:
The system employs parameter transformations that convert simple pulse wave measurements into multiple derived features including time differences, amplitude ratios, and statistical parameters. These parameter changes enable accurate identification using relatively simple measurement devices, as the computational processing extracts rich information from basic signals.
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 solution enables robust and accurate identification of biological subjects by analyzing pulse wave data with reduced noise influence and varying environmental conditions, providing a strong basis for building a reliable authentication system.
Implementation Method 1
a pulse wave measurement unit that measures a pulse wave of an identification target
Data Source
AI summary
An identification device including a generation unit and an information identification unit. The generation unit generates factor information relating to pulse-wave information with respect to an identification target in accordance with biological model information representing a relevance between pulse-wave information representing a pulse wave and the factor information representing a factor of pulse wave. The information identification unit selects certain list information satisfying a predetermined determination criterion of the factor information generated by the generation unit out of list information associating the factor information generated based on the pulse-wave information representing a pulse wave of a biological subject to be an identification target with identification information for identifying the biological subject, and identifies the identification information in the selected certain list information.