Driver fatigue detection based on prefrontal EEG using multi-entropy measures and hybrid model

Driver fatigue has become an important factor to traffic accidents worldwide, and effective detection of driver fatigue has major significance for public health. The purpose method employs entropy measures for feature extraction from a single electroencephalogram (EEG) channel. Four types of entropies measures, sample entropy (SE), fuzzy entropy (FE), approximate entropy (AE), and spectral entropy (PE), were deployed for the analysis of original EEG signal and compared by ten state-of-the-art classifiers. Results indicate that optimal performance of single channel is achieved using a combination of channel CP4, feature FE, and classifier Random Forest (RF). The highest accuracy can be up to 96.6%, which has been able to meet the needs of real applications. The best combination of channel + features + classifier is subject-specific. In this work, the accuracy of FE as the feature is far greater than the Acc of other features. The accuracy using classifier RF is the best, while that of classifier SVM with linear kernel is the worst. The impact of channel selection on the Acc is larger. The performance of various channels is very different.

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Driver Fatigue Detection Using Image Processing

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i3.780782 ◽

2019 ◽

Vol 7 (3) ◽

pp. 780-782

Author(s):

Radhika Raj ◽

Betsy Chacko

Keyword(s):

Image Processing ◽

Driver Fatigue ◽

Fatigue Detection

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A Real-time Driver Fatigue Detection Method Based on Two-Stage Convolutional Neural Network

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.2357 ◽

2020 ◽

Vol 53 (2) ◽

pp. 15374-15379

Author(s):

Hu He ◽

Xiaoyong Zhang ◽

Fu Jiang ◽

Chenglong Wang ◽

Yingze Yang ◽

...

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Real Time ◽

Detection Method ◽

Driver Fatigue ◽

Two Stage ◽

Fatigue Detection

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Driver fatigue detection based on mouth information

2010 8th World Congress on Intelligent Control and Automation ◽

10.1109/wcica.2010.5554645 ◽

2010 ◽

Cited By ~ 2

Author(s):

Lingling Li ◽

Yangzhou Chen ◽

Le Xin

Keyword(s):

Driver Fatigue ◽

Fatigue Detection

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Driver fatigue detection based on the distance of eyelid

Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, 2005. ◽

10.1109/iwvdvt.2005.1504626 ◽

2005 ◽

Cited By ~ 17

Author(s):

Wenhui Dong ◽

Xiaojuan Wu

Keyword(s):

Driver Fatigue ◽

Fatigue Detection

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Multi-pose Face Detection under Strong Light Condition for Android Driver Fatigue detection System

Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering ◽

10.2991/nceece-15.2016.266 ◽

2016 ◽

Author(s):

Guangmin Sun ◽

Qing Zhang ◽

Junjie Zhang

Keyword(s):

Face Detection ◽

Detection System ◽

Light Condition ◽

Driver Fatigue ◽

Strong Light ◽

Fatigue Detection

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Implementation of face and eye detection on DM6437 board using simulink model

Bulletin of Electrical Engineering and Informatics ◽

10.11591/eei.v9i2.1703 ◽

2020 ◽

Vol 9 (2) ◽

pp. 785-791

Author(s):

B. Vijayalaxmi ◽

Kaushik Sekaran ◽

N. Neelima ◽

P. Chandana ◽

Maytham N. Meqdad ◽

...

Keyword(s):

Research Work ◽

Warning Signal ◽

Blink Rate ◽

Driver Fatigue ◽

Simulink Model ◽

Detection Stage ◽

The Face ◽

Fatigue Estimation ◽

Motivating Factor ◽

Fatigue Detection

Driver Assistance system is significant in drriver drowsiness to avoid on road accidents. The aim of this research work is to detect the position of driver’s eye for fatigue estimation. It is not unusual to see vehicles moving around even during the nights. In such circumstances there will be very high probability that a driver gets drowsy which may lead to fatal accidents. Providing a solution to this problem has become a motivating factor for this research, which aims at detecting driver fatigue. This research concentrates on locating the eye region failing which a warning signal is generated so as to alert the driver. In this paper, an efficient algorithm is proposed for detecting the location of an eye, which forms an invaluable insight for driver fatigue detection after the face detection stage. After detecting the eyes, eye tracking for input videos has to be achieved so that the blink rate of eyes can be determined.

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