Driving fatigue detection based on fusion of EEG and vehicle motion information

Lan, Zhendong; Zhao, Jian; Liu, Pengbo; Zhang, Chi; Lyu, Nana; Guo, Lie

doi:10.1016/j.bspc.2024.106031

Cited by 7 publications

(1 citation statement)

References 38 publications

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“…One study explores the neurophysiological effects of visual, auditory, and higher-order cognitive distraction on young drivers using electroencephalograms (EEG) [7]. Another study also utilized EEG to calculate the correlation between brain neural activity and driving behavior [8]. The proposed scheme in Yang et al's stu dy accurately detects driver alertness using face landmarks, head poses, and gaze direction, offering high classification accuracy in actual road driving conditions [9].…”

Section: Introductionmentioning

confidence: 99%

Monitoring Physiological State of Drivers Using In-Vehicle Sensing of Non-Invasive Signal

Abdul Razak,

Sayed Ismail,

Hii Ben Bin

et al. 2024

Civ Eng J

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Eighty percent of traffic accidents are caused by human error, called hypo vigilance, stemming from drowsiness, stress, or distraction while driving. This poses a significant threat to road safety. An electrocardiogram (ECG) is often used to monitor drivers' health. Thus, enhancing vehicles with Internet of Things (IoT) sensors and local analytical databases becomes crucial for real-time detection and transmission of relevant health data to avoid things that compromise road traffic safety. This study introduces a cost-effective in-vehicle ECG sensing prototype using an AD8232 sensor integrated with an Arduino Uno and an AD8232 Wi-Fi module placed on the steering wheel to monitor the driver's heart signal while driving. Short-term heart rate variability (HRV) features were computed through Python from the acquired ECG data, and supervised machine learning techniques such as AdaBoost, Random Forest, Naïve Bayes, and Support Vector Machine (SVM) classified the features into normal and abnormal classes. Naive Bayes exhibited the highest accuracy (90.91%) and F1 score (85.71%), surpassing Random Forest's lower accuracy (63.64%) and F1 score (50.00%). These findings indicate the prototype's potential as a valuable tool for ensuring safe and efficient driving, proposing integration into standard vehicle safety systems for enhanced road traffic safety. Doi: 10.28991/CEJ-2024-010-04-014 Full Text: PDF

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Section: Introductionmentioning

confidence: 99%