Real-Time EEG-Based Detection of Fatigue Driving Danger for Accident Prediction

2015 ◽  
Vol 25 (02) ◽  
pp. 1550002 ◽  
Author(s):  
Hong Wang ◽  
Chi Zhang ◽  
Tianwei Shi ◽  
Fuwang Wang ◽  
Shujun Ma
Keyword(s):  
Real Time ◽  
Detection System ◽  
Neural Mechanism ◽  
Processing Capacity ◽  
Driver Fatigue ◽  
Accident Prediction ◽  
Functional Brain Networks ◽  
Long Time ◽  
Danger Detection ◽  
Fatigue Driving

This paper proposes a real-time electroencephalogram (EEG)-based detection method of the potential danger during fatigue driving. To determine driver fatigue in real time, wavelet entropy with a sliding window and pulse coupled neural network (PCNN) were used to process the EEG signals in the visual area (the main information input route). To detect the fatigue danger, the neural mechanism of driver fatigue was analyzed. The functional brain networks were employed to track the fatigue impact on processing capacity of brain. The results show the overall functional connectivity of the subjects is weakened after long time driving tasks. The regularity is summarized as the fatigue convergence phenomenon. Based on the fatigue convergence phenomenon, we combined both the input and global synchronizations of brain together to calculate the residual amount of the information processing capacity of brain to obtain the dangerous points in real time. Finally, the danger detection system of the driver fatigue based on the neural mechanism was validated using accident EEG. The time distributions of the output danger points of the system have a good agreement with those of the real accident points.

Driver Fatigue Detection Based On Facial Feature Analysis

2021 ◽  
pp. 2150034
Author(s):  
Yimin Zhang ◽  
Xianwei Han ◽  
Wei Gao ◽  
Yunliang Hu
Keyword(s):  
Traffic Accidents ◽  
Detection System ◽  
Recognition Rate ◽  
Ccd Camera ◽  
Facial Feature ◽  
The State ◽  
Driver Fatigue ◽  
Eyelid Closure ◽  
Fatigue Driving ◽  
Fatigue Detection

Fatigue driving is one of the main causes of traffic accidents. In recent years, considerable attention has been paid to fatigue detection systems, which is an important solution for preventing fatigue driving. In order to prevent and reduce fatigue driving, a driver fatigue detection system based on computer vision is proposed. In this system, an improved face detection method is used to detect the driver’s face from the image obtained by a charge coupled device (CCD) camera. Then, the feature points of the eyes and mouth are located by an ensemble of regression trees. Next, fatigue characteristic parameters are calculated by the improved percentage of eyelid closure over the pupil over time algorithm. Finally, the state of drivers is evaluated by using a fuzzy neural network. The system can effectively monitor and remind the state of drivers so as to significantly avoid or decrease the occurrence of traffic accidents. The experimental results show that the system is of wonderful real-time performance and accurate recognition rate, so it meets the requirements of practicality in driver fatigue detection greatly.

DETECTION OF DRIVING FATIGUE BY USING NONCONTACT EMG AND ECG SIGNALS MEASUREMENT SYSTEM

2014 ◽  
Vol 24 (03) ◽  
pp. 1450006 ◽  
Author(s):  
RONGRONG FU ◽  
HONG WANG
Keyword(s):  
Detection System ◽  
Biceps Femoris ◽  
Physiological Signals ◽  
Well Performance ◽  
Driver Fatigue ◽  
Training Samples ◽  
Fast Independent Component Analysis ◽  
Kolmogorov Smirnov ◽  
Driving Fatigue ◽  
Fatigue Driving

Driver fatigue can be detected by constructing a discriminant mode using some features obtained from physiological signals. There exist two major challenges of this kind of methods. One is how to collect physiological signals from subjects while they are driving without any interruption. The other is to find features of physiological signals that are of corresponding change with the loss of attention caused by driver fatigue. Driving fatigue is detected based on the study of surface electromyography (EMG) and electrocardiograph (ECG) during the driving period. The noncontact data acquisition system was used to collect physiological signals from the biceps femoris of each subject to tackle the first challenge. Fast independent component analysis (FastICA) and digital filter were utilized to process the original signals. Based on the statistical analysis results given by Kolmogorov–Smirnov Z test, the peak factor of EMG (p < 0.001) and the maximum of the cross-relation curve of EMG and ECG (p < 0.001) were selected as the combined characteristic to detect fatigue of drivers. The discriminant criterion of fatigue was obtained from the training samples by using Mahalanobis distance, and then the average classification accuracy was given by 10-fold cross-validation. The results showed that the method proposed in this paper can give well performance in distinguishing the normal state and fatigue state. The noncontact, onboard vehicle drivers' fatigue detection system was developed to reduce fatigue-related risks.

scholarly journals Real-time Alarm Monitoring System for Detecting Driver Fatigue in Wireless Areas

2017 ◽  
Vol 29 (2) ◽  
pp. 165-174 ◽  
Author(s):  
Rongrong Fu ◽  
Shutao Wang ◽  
Shiwei Wang
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Operating Characteristic ◽  
Roc Curves ◽  
Driver Fatigue ◽  
Relative Operating Characteristic ◽  
Theta Wave ◽  
Fatigue Driving ◽  
Grey Relational ◽  
Electroencephalogram Eeg

The purpose of this paper was to develop a real-time alarm monitoring system that can detect the fatigue driving state through wireless communication. The drivers’ electroencephalogram (EEG) signals were recorded from occipital electrodes. Seven EEG rhythms with different frequency bands as gamma, hbeta, beta, sigma, alpha, theta and delta waves were extracted. They were simultaneously assessed using relative operating characteristic (ROC) curves and grey relational analysis to select one as the fatigue feature. The research results showed that the performance of theta wave was the best one. Therefore, theta wave was used as fatigue feature in the following alarm device. The real-time alarm monitoring system based on the result has been developed, once the threshold was settled by using the data of the first ten minutes driving period. The developed system can detect driver fatigue and give alarm to indicate the onset of fatigue automatically.

scholarly journals Real Time Driver Fatigue Detection System Based on Multi-Task ConNN

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 12491-12498 ◽  
Author(s):  
Burcu Kir Savas ◽  
Yasar Becerikli
Keyword(s):  
Real Time ◽  
Detection System ◽  
Driver Fatigue ◽  
Fatigue Detection

scholarly journals Design and research on the fatigue detection system of ship bridge duty based on image processing

2021 ◽  
Vol 2131 (3) ◽  
pp. 032119
Author(s):  
Yonggang Zong ◽  
Xiandong Zhao ◽  
Zhongfeng Ba
Keyword(s):  
Image Processing ◽  
Real Time ◽  
Early Warning ◽  
Performance Indicators ◽  
Large Scale ◽  
Detection System ◽  
Warning System ◽  
Technical Standards ◽  
Eyelid Closure ◽  
Fatigue Driving

Abstract With the development of the marine economy, the number of ships is increasing day by day, and is developing towards large-scale, diversified and professional development, and marine accidents caused by driver fatigue have attracted more and more attention. In order to reduce marine traffic accidents caused by fatigue driving of ship drivers and ensure the safety of life and property at sea, it is very necessary and important to study effective methods to detect the fatigue state of ship drivers in real time. This article mainly studies the early warning of ship fatigue driving. In view of the difficulties of the ship fatigue driving detection technology, reasonable performance indicators of the ship anti-fatigue driving image processing and early warning system are proposed; according to the system performance indicators, the HOG+SVM method is determined to automatically track the human face, and the human eye detection and tracking method is designed. Improved the method of eyelid closure to determine fatigue. In order to determine the eye opening and closing state or blinking frequency. The PERCLOS method is used to determine whether the driver is tired, and a warning is given when the ship’s watch driver is tired. The system has the characteristics of non-contact, real-time, etc. and complies with the relevant technical standards of the International Maritime Organization (IMO) on the ship bridge fatigue warning system (BNWAS).

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