ECG Authentication System Dynamic Threshold Adaptation
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Solution Overview
Problem
Existing biometric authentication methods using electrocardiogram (ECG) signals face challenges in accurately distinguishing between varying ECG signals from the same user due to changes in physiological and mental states, leading to potential false authentication and identification issues.
Innovation Solution
A method that involves identifying and comparing ECG signals using a reference ECG signal set, determining an authentication threshold through a neural network-based feature vector, and dynamically updating the reference set based on authentication success or failure to enhance accuracy and adapt to user state variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If ECG signals are used for user authentication, then high identification rates and stability are achieved, but false authentication occurs due to variations in physiological and mental states
Solution Approach 1:
The patent implements a dynamic reference ECG signal set that is continuously updated based on authentication results. The system adapts to users' changing physiological and mental states by incorporating newly authenticated ECG signals into the reference set, making the authentication system dynamic rather than static. This resolves the contradiction by allowing the system to maintain high reliability while accommodating natural signal variations.
Solution Approach 2:
The patent dynamically adjusts the authentication threshold based on the size and composition of the reference ECG signal set. As the reference set grows or changes, the threshold is recalibrated to maintain optimal authentication accuracy. This parameter adjustment mechanism allows the system to distinguish between genuine signal variations and actual authentication failures, resolving the contradiction between reliability and measurement precision.
2Measurement precision
If a fixed reference ECG signal set is used, then authentication processing is simple, but identification accuracy decreases due to user state variations
Solution Approach 1:
The patent implements a self-updating authentication system where the reference ECG signal set automatically expands and adapts based on authentication outcomes. Successfully authenticated ECG signals are automatically added to the reference set, allowing the system to serve itself by continuously improving its own reference data without requiring manual intervention. This maintains relatively simple processing while improving identification accuracy through automatic adaptation.
Solution Approach 2:
The patent incorporates feedback mechanisms where authentication results directly influence future authentication processes. The system uses authentication outcomes to update the reference ECG signal set and adjust thresholds, creating a closed-loop system that continuously learns and improves. This feedback-driven approach enhances identification accuracy while keeping the overall system structure relatively simple through automated decision-making.
3Reliability
If authentication threshold is set high to prevent false authentication, then security improves, but legitimate users may be rejected due to signal variations
Solution Approach 1:
The patent dynamically adjusts the authentication threshold based on the reference ECG signal set characteristics and authentication performance. Rather than using a fixed high threshold, the system adapts the threshold level to balance security and acceptance rates. This parameter flexibility allows the system to maintain high false authentication prevention while accommodating legitimate signal variations through adaptive threshold calibration.
Solution Approach 2:
The patent implements a dynamic authentication system where both the reference signal set and threshold adapt over time. This dynamic approach allows the system to become more permissive or more strict based on accumulated data and performance metrics, resolving the contradiction between preventing false authentication and accepting legitimate users. The system evolves to find the optimal balance between security and usability.
Data Source
AI summary
A method and apparatus to authenticate a registered user are described. The method and apparatus include a processor configured to identify a first electrocardiogram (ECG) signal measured from the user, and determine a similarity between the first ECG signal and a second ECG signal based on the identified first ECG signal and the second ECG signal included in a reference ECG signal set. The processor is also configured to determine an authentication threshold corresponding to the reference ECG signal set, and determine whether to authenticate the first ECG signal measured from the user by comparing the determined similarity and the authentication threshold.