Compressed Physiological Signal Identity Recognition System
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Solution Overview
Problem
Existing identity recognition systems using compressive sensing require significant time and resources for signal reconstruction, limiting their convenience and efficiency.
Innovation Solution
An identity recognition system that employs a sensing wearable device to continuously measure and compress physiological signals, transmitting the compressed signals to an identification computer device for recognition using a classification model based on principal eigenvectors, without the need for signal reconstruction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If compressed signal reconstruction is performed using traditional methods, then signal accuracy is maintained, but time consumption and computational resources increase significantly
Solution Approach 1:
The patent applies preliminary action by pre-calculating and storing the projection matrix and its transpose in the sensing wearable device before actual signal acquisition. This pre-computation eliminates the need for complex reconstruction calculations during real-time operation, allowing direct mapping of compressed signals to original signal space while maintaining accuracy and significantly reducing reconstruction time.
Solution Approach 2:
The patent introduces a projection matrix as an intermediary tool that facilitates direct transformation between compressed and original signal domains. By pre-computing this matrix and storing it locally, the system creates an efficient intermediary mechanism that enables accurate signal recovery without traditional iterative reconstruction algorithms, thus resolving the contradiction between accuracy and time consumption.
2Measurement precision
If full-resolution physiological signals are transmitted continuously, then recognition accuracy is maintained, but energy consumption increases
Solution Approach 1:
The patent extracts only the essential identity-related features from physiological signals through compressive sensing, transmitting only the compressed representation rather than full-resolution signals. This extraction approach maintains sufficient recognition accuracy by preserving discriminative features while dramatically reducing data transmission volume and associated energy consumption.
Solution Approach 2:
The patent changes the parameter of signal representation from full-resolution time-domain signals to compressed-domain representations with reduced dimensionality. By transforming the signal into a compressed form that retains identity-discriminative information, the system maintains recognition accuracy while reducing transmission energy requirements.
3Use of energy by moving object
If signal compression is applied to reduce transmission data, then energy consumption decreases, but signal reconstruction complexity increases
Solution Approach 1:
The patent resolves reconstruction complexity by performing all complex matrix operations in advance and storing the results locally. The pre-computed projection matrix enables simple direct multiplication operations during actual signal processing, eliminating the need for complex iterative reconstruction algorithms and reducing computational complexity while maintaining compression benefits.
4Measurement precision
If traditional compressive sensing reconstruction algorithms are used, then signal recovery accuracy is achieved, but computational resources are consumed heavily
Solution Approach 1:
The patent eliminates heavy computational power requirements by pre-computing the projection matrix and storing it locally in the sensing wearable device. This allows the system to perform simple matrix multiplication operations during signal processing instead of running computationally intensive iterative reconstruction algorithms, thus achieving accurate signal recovery with minimal computational power consumption.
Data Source
AI summary
An identity recognition system based on compressed signals and a method thereof are provided. When a sensing end is in an identification mode, it continuously measures a physiological identification signal of a user having at least one first predetermined length of time, and performs a compression process on the physiological identification signal having at least one first predetermined length of time to generate a first compressed signal. An identification end receives the first compressed signal, obtains first identification information of the first compressed signal in a discrimination subspace according to a principal eigenvector, and recognizes the first identification signal according to a classification model from a learning end to determine that the user is one of multiple subjects. Therefore, the sensing end uses compressive technology to reduce the energy required to transmit signals, and the identification end recognizes identity without reconstructing the first compressed signal transmitted by the sensing end.