Detection of alertness-related EEG signals based on decision fused BP neural network

Zhang, Meiyan; Liú, Dan; Wang, Qisong; Zhao, Boqi; Bai, Ou; Sun, Jinwei

doi:10.1016/j.bspc.2022.103479

Cited by 15 publications

(3 citation statements)

References 25 publications

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“…Driver drowsiness detection techniques fall into three main categories. The first category is the biological feature technique that involves analyzing physiological signals [15], skin temperature, and galvanic skin response (GSR) to measure physical conditions that change with the level of drowsiness or fatigue [16][17][18][19]. The second category is vehicle movement indicator techniques specifically focused on driving applications to detect abnormal driving behavior due to fatigue or drowsiness, such as random braking, lane positioning, abnormal speeding, and abnormal steering.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The second category is vehicle movement indicator techniques specifically focused on driving applications to detect abnormal driving behavior due to fatigue or drowsiness, such as random braking, lane positioning, abnormal speeding, and abnormal steering. We can observe these kinds of behavior with the help of different sensors in the vehicle [2,19,20]. Vehicle movement indicator techniques have several restrictions such as road shape, vehicle type, driver expertise, and the situation, and more importantly, it needs more time to acquire all these parameters [14,21].…”

Section: Literature Reviewmentioning

confidence: 99%

“…The study [19] proposed a reliable fatigue detection system built on the CNN model. The study also proposed a fusion method to measure multiple physical features.…”

Section: Literature Reviewmentioning

confidence: 99%

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Detection of Drowsiness among Drivers Using Novel Deep Convolutional Neural Network Model

Majeed,

Shafique,

Safran

et al. 2023

Sensors

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Detecting drowsiness among drivers is critical for ensuring road safety and preventing accidents caused by drowsy or fatigued driving. Research on yawn detection among drivers has great significance in improving traffic safety. Although various studies have taken place where deep learning-based approaches are being proposed, there is still room for improvement to develop better and more accurate drowsiness detection systems using behavioral features such as mouth and eye movement. This study proposes a deep neural network architecture for drowsiness detection employing a convolutional neural network (CNN) for driver drowsiness detection. Experiments involve using the DLIB library to locate key facial points to calculate the mouth aspect ratio (MAR). To compensate for the small dataset, data augmentation is performed for the ‘yawning’ and ‘no_yawning’ classes. Models are trained and tested involving the original and augmented dataset to analyze the impact on model performance. Experimental results demonstrate that the proposed CNN model achieves an average accuracy of 96.69%. Performance comparison with existing state-of-the-art approaches shows better performance of the proposed model.

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