A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

Abstract

Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.

Physiological-based Driver Monitoring Systems: A Scoping Review

“…Earlier, two characteristics of EEG signals i.e., power spectral density (PSD) and sample entropy (SampEn) were integrated to assess cognitive fatigueness [52]. Integration of EEG signals with other features including facial expressions, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS) were also implemented to determine a driver fatigue state [53].…”

“…In another work, a fast support vector machine (FSVM) algorithm based on EEG and EOG modal data was implemented to recognize symptoms of driver fatigueness. When a symptom is visible, the driver and nearby vehicles will be informed via IoT technology [53]. On the other hand, the EEG signals were first reduced using the Weighted Principal Component Analysis (WPCA) algorithm before applying SVM to detect fatigue driver.…”

The evaluation of cEEGrids for fatigue detection in aviation

“…Additionally, Huang et al [ 26 ] demonstrated the feasibility of EEG as a real-time fatigue detection and alerting system by examining alpha and theta power. However, even though EEG is frequently used to detect fatigue, the conventional setup is unsuitable for use in flight-related operations due to, for instance, the lengthy preparation time and limited mobility of the attached cap and electrodes] [ 3 , 14 , 27–29 ]. Nevertheless, the technology is still evolving, and manufacturers are increasingly developing innovative solutions to make EEG less invasive and more mobile [ 3 , 5 , 28 , 30 ].…”

Scaling effectivity in manifold methodologies to detect driver’s fatigueness and drowsiness state

“…Kashani et al [13] have presented a framework based on data mining where tree is constructed towards performing classification and regression for investigating the prime indicators of fatigued-based accidents. Liu et al [14] have developed a solution model by combining two different physiological attributes related to signals of heart and eye blinks in order to recognize the fatigued state of driver. Model constructed by Zhao et al [15] have contributed towards detecting the level of distraction among the driver on the basis of estimated head poses of driver.…”