Fatigue driving recognition based on deep learning and graph neural network

Lin, Zhiqiang; Qiu, Taorong; Ping, Liu; Zhang, Lingyun; Zhang, Siwei; Zhang, Mu

doi:10.1016/j.bspc.2021.102598

Cited by 26 publications

(12 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hajinoroozi et al [39] Spatial correlations CNN 86.14% Gao et al [36] Recurrence network CNN 92.95% Lin et al [42] Noise distribution CNN 93% Zhao et al [47] The region of interest (ROI) EM-CNN 93.62% Yang et al [48] Multi-column CNN 90.65% Ed-Doughmi et al [49] Multi-layer model 3D-CNN 92% Our proposed method RPSD CNN 94.51% For further analysis, t-SNE (t-Distributed Stochastic Neighbor Embedding) analysis was performed. In this study, t-SNE was used to visually compare differences before and after entering the RPSD-CNN model.…”

Section: Classification Methods Resultsmentioning

confidence: 99%

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Xu,

Li,

Feng

2023

Sensors

View full text Add to dashboard Cite

Fatigue of miners is caused by intensive workloads, long working hours, and shift-work schedules. It is one of the major factors increasing the risk of safety problems and work mistakes. Examining the detection of miner fatigue is important because it can potentially prevent work accidents and improve working efficiency in underground coal mines. Many previous studies have introduced feature-based machine-learning methods to estimate miner fatigue. This work proposes a method that uses electroencephalogram (EEG) signals to generate topographic maps containing frequency and spatial information. It utilizes a convolutional neural network (CNN) to classify the normal state, critical state, and fatigue state of miners. The topographic maps are generated from the EEG signals and contrasted using power spectral density (PSD) and relative power spectral density (RPSD). These two feature extraction methods were applied to feature recognition and four representative deep-learning methods. The results showthat RPSD achieves better performance than PSD in classification accuracy with all deep-learning methods. The CNN achieved superior results to the other deep-learning methods, with an accuracy of 94.5%, precision of 97.0%, sensitivity of 94.8%, and F1 score of 96.3%. Our results also show that the RPSD–CNN method outperforms the current state of the art. Thus, this method might be a useful and effective miner fatigue detection tool for coal companies in the near future.

show abstract

Section: Classification Methods Resultsmentioning

confidence: 99%

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Xu,

Li,

Feng

2023

Sensors

View full text Add to dashboard Cite

show abstract

“…According to previous studies, the number of subjects in the experiments was in the range of 10-38 [21][22][23][24][25]. e number of subjects in this study is 36, which could meet the minimum sample size requirement.…”

Section: Subjectsmentioning

confidence: 93%

Monitoring Driving in a Monotonous Environment: Classification and Recognition of Driving Fatigue Based on Long Short-Term Memory Network

Han

2022

Journal of Advanced Transportation

View full text Add to dashboard Cite

The driver is one of the most important factors in road traffic. Monitoring the driver’s driving status can greatly improve the safety and road operation efficiency of urban road traffic in the case of multiple traffic modes. Fatigue has a significant impact on drivers’ safety on the road, particularly while driving in a monotonous environment for a long time. In this study, the eye movement parameters of 36 drivers were collected through the simulation experiment of a driving simulator. The pupil area and percentage of eye closure (PERCLOS) in driving scenes of the expressway and low-grade rural road were combined with the Stanford Sleepiness Scale (SSS) to determine the threshold of fatigue degree in different monotonous driving scenarios. A recognition model of different fatigue degrees of drivers is built based on the deep learning method of a long short-term memory network (LSTM) to detect the varied fatigue degrees of drivers. The result shows that the fatigue degree of drivers increases as driving time increases on both expressways and low-grade rural roads. In the same driving time, the driver felt tired faster on the expressway, and the fatigue degree was significantly higher than that on the country road. The recognition rate of the established fatigue degree recognition model for driver’s awake state, mild fatigue, moderate fatigue, and severe fatigue is 100%, 93.1%, 98.4%, and 100% respectively, and the total recognition rate can reach 97.8%, which is higher than the recognition accuracy of the traditional machine learning approach.

show abstract

“…Ye et al [ 2 ] proposed a fatigue driving state recognition method based on sample entropy and kernel principal component analysis, which combined the advantages of high recognition accuracy of sample entropy and strong processing capability of kernel principal component analysis in nonlinear principal component reduction and nonlinearity and achieved good results. Lin et al [ 3 ] proposed a method for the dynamic construction of functional brain networks based on singular value entropy and fractal dimensionality. The experimental results showed that the method has high accuracy in fatigue driving recognition.…”

Section: Introductionmentioning

confidence: 99%

A New Feature Analysis Approach to Selecting Channels of EEG for Fatigue Driving

Liao

Shangguan

Peng

et al. 2022

Computational and Mathematical Methods in Medicine

Self Cite

View full text Add to dashboard Cite

Fatigued driving is a significant contributor to traffic accidents. There are some issues with common EEG data of 32 channels, 64 channels, and 128 channels, such as difficult acquisition, high data redundancy, and difficult practical application. A new channel selection method called ReliefF_SFS is proposed to address the problem of how to reduce the number of channels while maintaining classification accuracy. It combines the ReliefF algorithm and the sequential forward selection (SFS) algorithm. When only T6, O1, Oz, T4, P3, and FC3 are used, the classification accuracy under Theta_Std+FE combined with ReliefF_SFS achieves 99.45%. The strategy suggested in this paper not only ensures the recognition accuracy but also reduces the number of channels when compared to other models based on the same data set.

show abstract

Fatigue driving recognition based on deep learning and graph neural network

Cited by 26 publications

References 22 publications

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Miner Fatigue Detection from Electroencephalogram-Based Relative Power Spectral Topography Using Convolutional Neural Network

Monitoring Driving in a Monotonous Environment: Classification and Recognition of Driving Fatigue Based on Long Short-Term Memory Network

A New Feature Analysis Approach to Selecting Channels of EEG for Fatigue Driving

Contact Info

Product

Resources

About