Detection of Train Driver Fatigue and Distraction Based on Forehead EEG: A Time-Series Ensemble Learning Method

Fan, Chaojie; Peng, Yong; Peng, Shuangling; Zhang, Honghao; Wu, Yao

doi:10.1109/tits.2021.3125737

Cited by 64 publications

(25 citation statements)

References 49 publications

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“…The mean absolute error (MAE), root mean square error (RMSE), and goodness of fit (R-squared) are 70.14 (± 13.02) ms, 102.19 (± 22.18) ms, and 0.74 (± 0.09) for the estimated reaction time, respectively. Fan et al [ 63 ] collected EEG signals from an EEG recording device placed on the driver's forehead and extracted many features from the EEG signals, including energy, entropy, rhythm-energy ratio, and frontal asymmetry ratio, and proposed a time-series ensemble learning method for detecting the fatigue state of train drivers. This study is the first to detect train driver fatigue and distraction simultaneously.…”

Section: Detection Methods Of Train Driver Fatigue and Distractionmentioning

confidence: 99%

Types, Risk Factors, Consequences, and Detection Methods of Train Driver Fatigue and Distraction

Fan

Huang

Lin

et al. 2022

Computational Intelligence and Neuroscience

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Train drivers’ inattention, including fatigue and distraction, impairs their ability to drive and is the major risk factor for human-caused train accidents. Many experts have undertaken numerous studies on train driver exhaustion and distraction, but a systematic study is still missing. Through a systematic review, this work aims to outline the types, risk factors, consequences, and detection methods of train driver fatigue and distraction. The effects of central nervous fatigue and cognitive distraction in train drivers during driving are caused by rest and sleep schedules, workload, automation levels, and mobile phones. Furthermore, train drivers’ fatigue and distraction can cause loss of concentration and slow reaction, resulting in dangerous driving behaviour such as speeding and SPAD. Researchers have combined subjective reporting, physiological parameters, and physical factors to construct detection algorithms with good results to detect train driver fatigue and distraction. This review offers recommendations for researchers looking into train driver fatigue and distraction. And it can also make valuable recommendations for future studies about railway traffic safety.

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Section: Detection Methods Of Train Driver Fatigue and Distractionmentioning

confidence: 99%

Types, Risk Factors, Consequences, and Detection Methods of Train Driver Fatigue and Distraction

Fan

Huang

Lin

et al. 2022

Computational Intelligence and Neuroscience

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“…The observation of “resource consumption degree” is to ensure that the consumption degree of production materials is within the scope of the resource use plan. On the one hand, the overconsumption behavior [ 40 ] is found in time, and the response is made to avoid the delay phenomenon caused by the shortage of production materials [ 41 ]. On the other hand, summarize the key measures conducive to resource-saving (such as intelligent control, process optimization, etc.)…”

Section: Model Construction: Based On the System Dynamic Methodsmentioning

confidence: 99%

The Promotion Strategy Analysis for Green Degree of Railway Engineering Based on a System Dynamic Flow Diagram

Tang

Wang

Tang

et al. 2022

Journal of Environmental and Public Health

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This article made a system dynamics flow diagram (SD flow diagram) to describe the green railway engineering (GRE) system, which provides a theoretical basis for discussing the source and change process of the green degree of railway engineering(GDR) and also provides a practical basis for accurate policy implementation and evaluation promotion of GRE management. Based on the definition of GDR and using “input-output” relationship to analyze system structure of GRE, set two green goals of environmental and resource cost decreases as the clue, deconstructed practice process based on the principle of construction to form GRE system dynamic flow diagram, which aims to reveal the key influencing factors and promotion path of GDR. The results of the research show that (1) the green schemes set the foundation of GDR, including 3 schemes of green planning, green design, green construction, and determine the expected control values (VE) of 4 status, namely ecological damage degree, environmental pollution degree, land occupation degree, and resources consume degree. (2) The deviation of expected control values (VE) and actual control values (VA) from 4 status is the premise of whether the GDR needs to be optimized or improved, and 2 practice achievements of green knowledge innovation and green culture creation provided different promotion paths for GDR. (3) According to the SD flow diagram constructed by research, the 3 schemes are influenced by regional ecological carrying capacity, social material resource reserve, green knowledge reserve, green talent reserve , reasonable goals setting, strengthening preliminary research, making full use of resources, deepening the connection of procedures, and so on are conducive to build a foundation for GDR. (4) The 4 status are directly controlled by seven rate variables, which promote the dynamic optimization of GDR by technology, equipment, institution management, and behavior management. The SD flow diagram of GRE provides 2 contributions. The first provides an analytical basis for the study of the promotion strategy of GDR, and the second provides a model basis for further quantitative study of GDR.

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“…fNIRS is an optical brain monitoring technique which uses near-infrared spectroscopy to estimate cortical hemodynamic activity which occur in response to neural activities ( Sun and Liao, 2019 ; Liao et al, 2021 ). These two methods can both capture rich cognitive information in brain activities ( Fan et al, 2021 ). Therefore, they have been applied to explore the cognitive process of construction workers during hazard recognition.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of Eye-Tracking Studies of Construction Safety

et al. 2022

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Safety is the most important concern in the construction industry, and construction workers’ attention allocation is closely associated with their hazard recognition and safety behaviors. The recent emergence of eye-tracking techniques allows researchers in construction safety to further investigate construction workers’ visual attention allocation during hazard recognition. The existing eye-tracking studies in construction safety need to be comprehensively understood, to provide practical suggestions for future research and on-site safety management. This study aims to summarize previous studies on the application of eye-tracking techniques to the construction safety context through a systematic literature review. The literature search and study selection process included 22 eligible studies. Content analysis was then carried out from participant selection, device selection, task design, area of interest determination, feature extraction, data analysis, and main findings. Major limitations of the existing studies are identified, and recommendations for future research in theoretical development, experiment improvement, and data analysis method advancement are proposed to address these limitations. Even though the application of eye-tracking techniques in construction safety research is still in its early stage, it is worth future continuous attention because relevant discoveries would be of great significance to hazard control and safety management in the construction industry.

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Detection of Train Driver Fatigue and Distraction Based on Forehead EEG: A Time-Series Ensemble Learning Method

Cited by 64 publications

References 49 publications

Types, Risk Factors, Consequences, and Detection Methods of Train Driver Fatigue and Distraction

Types, Risk Factors, Consequences, and Detection Methods of Train Driver Fatigue and Distraction

The Promotion Strategy Analysis for Green Degree of Railway Engineering Based on a System Dynamic Flow Diagram

A Systematic Review of Eye-Tracking Studies of Construction Safety

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