Research on fatigue driving detection using forehead EEG based on adaptive multi-scale entropy

Luo, Haowen; Qiu, Taorong; Liu, Chao; Huang, Peifan

doi:10.1016/j.bspc.2019.02.005

Cited by 87 publications

(48 citation statements)

References 29 publications

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“…Hence, it is necessary to effectively relieve driving fatigue. Studies have shown that repetitive and monotonous external environmental information can easily lead human beings to be in a state of mental fatigue, in which our brain activity is inhibited [50,51,52]. In our study, we tried to stimulate human brain nerves repeatedly to keep them active all the time to combat mental fatigue.…”

Section: Discussionmentioning

confidence: 99%

“…The research by Rongrong demonstrated that the fatigue parameter (posterior probabilities) showed an overall upward trend over time [3]. In the study of Luo, 5′ EEG signals, which were used to analyze the variation characteristics of driving fatigue, were saved in each experimental period [52]. In the whole experimental process, the experimental stages are evenly divided, and in each experimental stage, the subjects’ driving signals are detected for a short time.…”

Section: Discussionmentioning

confidence: 99%

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Study on the Effect of Man-Machine Response Mode to Relieve Driving Fatigue Based on EEG and EOG

Wang

2019

Sensors

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Rapid and accurate detection of driver fatigue is of great significance to improve traffic safety. In the present work, we propose the man-machine response mode (MRM) to relieve driver fatigue caused by long-term driving. In this paper, the characteristics of the complex brain network, which can effectively reflect brain activity information, were used to detect the change of driving fatigue over time. Combined with the traditional eye movement characteristics and a subjective questionnaire (SQ), the changes in driving fatigue characteristics were comprehensively analyzed. The results show that driving fatigue can be effectively delayed using the MRM. Additionally, the response equipment is low in cost and practical, so it will be practical to use in actual driving situations in the future.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Study on the Effect of Man-Machine Response Mode to Relieve Driving Fatigue Based on EEG and EOG

Wang

2019

Sensors

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“…The authors focused on the development of the driver’s fatigue system using non-visual feature-based techniques, such as EEG, ECG, and EOG control signals [ 141 , 142 , 143 , 144 ], since there was a large involvement of noise and artifacts added to the input signals. As a result, those signals are difficult to eliminate from the real-time driver signals.…”

Section: Iot-based Architectures For Dfd Systemsmentioning

confidence: 99%

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Abbas

Alsheddy

2020

Sensors

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Internet of things (IoT) cloud-based applications deliver advanced solutions for smart cities to decrease traffic accidents caused by driver fatigue while driving on the road. Environmental conditions or driver behavior can ultimately lead to serious roadside accidents. In recent years, the authors have developed many low-cost, computerized, driver fatigue detection systems (DFDs) to help drivers, by using multi-sensors, and mobile and cloud-based computing architecture. To promote safe driving, these are the most current emerging platforms that were introduced in the past. In this paper, we reviewed state-of-the-art approaches for predicting unsafe driving styles using three common IoT-based architectures. The novelty of this article is to show major differences among multi-sensors, smartphone-based, and cloud-based architectures in multimodal feature processing. We discussed all of the problems that machine learning techniques faced in recent years, particularly the deep learning (DL) model, to predict driver hypovigilance, especially in terms of these three IoT-based architectures. Moreover, we performed state-of-the-art comparisons by using driving simulators to incorporate multimodal features of the driver. We also mention online data sources in this article to test and train network architecture in the field of DFDs on public available multimodal datasets. These comparisons assist other authors to continue future research in this domain. To evaluate the performance, we mention the major problems in these three architectures to help researchers use the best IoT-based architecture for detecting DFDs in a real-time environment. Moreover, the important factors of Multi-Access Edge Computing (MEC) and 5th generation (5G) networks are analyzed in the context of deep learning architecture to improve the response time of DFD systems. Lastly, it is concluded that there is a research gap when it comes to implementing the DFD systems on MEC and 5G technologies by using multimodal features and DL architecture.

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“…The traditional single-scale entropy cannot adequately reflect the fatigue characteristics in EEG signals. Therefore, in recent years, more and more scholars have used the multiscale entropy method to extract EEG features [43]. Zou et al proposed a multi-scale entropy-based empirical mode decomposition (EMD) method to identify the characteristics of driving fatigue, and the results show that this method can effectively detect driving fatigue [44].…”

Section: Introductionmentioning

confidence: 99%

Research on Driving Fatigue Alleviation Using Interesting Auditory Stimulation Based on VMD-MMSE

Wang

Lü

Kang

et al. 2021

Entropy

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The accurate detection and alleviation of driving fatigue are of great significance to traffic safety. In this study, we tried to apply the modified multi-scale entropy (MMSE) approach, based on variational mode decomposition (VMD), to driving fatigue detection. Firstly, the VMD was used to decompose EEG into multiple intrinsic mode functions (IMFs), then the best IMFs and scale factors were selected using the least square method (LSM). Finally, the MMSE features were extracted. Compared with the traditional sample entropy (SampEn), the VMD-MMSE method can identify the characteristics of driving fatigue more effectively. The VMD-MMSE characteristics combined with a subjective questionnaire (SQ) were used to analyze the change trends of driving fatigue under two driving modes: normal driving mode and interesting auditory stimulation mode. The results show that the interesting auditory stimulation method adopted in this paper can effectively relieve driving fatigue. In addition, the interesting auditory stimulation method, which simply involves playing interesting auditory information on the vehicle-mounted player, can effectively relieve driving fatigue. Compared with traditional driving fatigue-relieving methods, such as sleeping and drinking coffee, this interesting auditory stimulation method can relieve fatigue in real-time when the driver is driving normally.

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Research on fatigue driving detection using forehead EEG based on adaptive multi-scale entropy

Cited by 87 publications

References 29 publications

Study on the Effect of Man-Machine Response Mode to Relieve Driving Fatigue Based on EEG and EOG

Study on the Effect of Man-Machine Response Mode to Relieve Driving Fatigue Based on EEG and EOG

Driver Fatigue Detection Systems Using Multi-Sensors, Smartphone, and Cloud-Based Computing Platforms: A Comparative Analysis

Research on Driving Fatigue Alleviation Using Interesting Auditory Stimulation Based on VMD-MMSE

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