Driver State Detection via EEG, ECG, and fNIRS Signal Fusion
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Solution Overview
Problem
Existing methods for detecting a driver's drowsy state using biometric signals face challenges in accurately determining the state due to noise interference in collected signals, making it difficult to diagnose fatigue and emotional states effectively.
Innovation Solution
An apparatus and method utilizing a combination of electro-encephalography (EEG), electro-cardiography (ECG), and functional near-infrared spectroscopy (fNIRS) to measure and integrate biometric signals, extract characteristics, and classify the driver's state, applying equal weights to EEG, ECG, and blood flow rate to quantify fatigue levels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple biometric signals (EEG, ECG, fNIRS) are collected to improve driver state detection accuracy, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple biometric sensors (EEG, ECG, fNIRS) into an integrated driver state detection system. The sensors are merged to collect complementary physiological signals simultaneously, allowing comprehensive analysis of driver fatigue and emotional states through signal fusion and integrated processing.
Solution Approach 2:
The detection system is designed with multi-functionality to detect various driver states including fatigue, drowsiness, and emotional conditions. The same sensor array and processing system can identify different physiological states by analyzing patterns in the collected biometric signals, making the system universally applicable to multiple detection purposes.
2Reliability
If biometric signals are collected from multiple sources to reduce noise interference, then reliability is improved, but device complexity increases
Solution Approach 1:
The patent introduces signal processing algorithms and integration mechanisms as intermediaries between the multiple biometric sensors and the final detection output. These intermediaries process, filter, and fuse the signals from EEG, ECG, and fNIRS sensors, reducing noise interference and extracting meaningful physiological information while managing the complexity of multiple signal sources.
3Measurement precision
If characteristics are extracted and integrated from multiple biometric signals to quantify fatigue level, then measurement precision is improved, but loss of information increases
Solution Approach 1:
The system extracts specific characteristic features from the raw biometric signals such as EEG frequency bands, ECG intervals, and fNIRS oxygenation levels. By taking out and isolating these relevant characteristics, the system achieves precise fatigue level quantification while discarding redundant or irrelevant signal components that would otherwise contribute to information loss.
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
This approach enables accurate classification and analysis of a driver's state, effectively quantifying fatigue levels by reducing noise interference and providing reliable feedback on the driver's condition.
Implementation Method 1
an emitter configured to generate near-field infrared ray to measure the blood flow rate of the brain
Implementation Method 2
a detector configured to detect the near-field infrared ray reflected after the emitter generates the near-field infrared ray and to obtain electrical signals
Implementation Method 3
an electro-encephalography measuring apparatus configured to measure the electro-encephalography occurring from the brain
Data Source
AI summary
An apparatus and a method is provided for detecting biometric signals of a driver and classifying the driver into a normal state or a fatigued state based on the biometric signals. An apparatus may include: a biometric signal measuring part configured to measure the biometric signals including a blood flow rate of a brain of the driver using an electro-encephalography (EEG), an electro-cardiography (ECG), and a functional near-infrared spectroscopy (fNIRS) of the driver; a biometric signal integral part configured to integrate the measured biometric signals, to extract characteristics of the respective biometric signals from the measured biometric signals and to then integrate the extracted characteristics, or to classify the extracted characteristics of the biometric signals and to then integrate the classified characteristics; and a driver state detecting part configured to detect the state of the driver based on the integrated biometric signals.