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With the development of rail transit, driver vigilance is increasingly important in railway safety. A vigilance detection method based on high-speed rail (HSR) is presented in this study. The proposed method includes three main parts: (i) a wireless wearable electroencephalography (EEG) collection module; (ii) HSR driver's vigilance detection modul...
Contexts in source publication
Context 1
... this study, an early warning module is devised. If drowsiness state is detected, two vibrators are used to wake up the HSR driver. The vibrator is shown in Fig. 5. Two vibrators with high mechanical vibrational frequency (100-200 Hz), which are mounted separately on both sides of the EEG data collection cap, can induce shock to the hat of the HSR driver to wake up. Its size is small so it could be fixed on both sides of the hat and will not affect the comfort of the cap. If the driver is judged to be drowsy, two vibrators are activated. Simultaneously, an indicator signal will be sent to the TCC regarding the HSR driver's vigilance level. The TCC will make judgment according to different ...
Context 2
... this study, an early warning module is devised. If drowsiness state is detected, two vibrators are used to wake up the HSR driver. The vibrator is shown in Fig. 5. Two vibrators with high mechanical vibrational frequency (100-200 Hz), which are mounted separately on both sides of the EEG data collection cap, can induce shock to the hat of the HSR driver to wake up. Its size is small so it could be fixed on both sides of the hat and will not affect the comfort of the cap. If the driver is judged to be drowsy, two vibrators are activated. Simultaneously, an indicator signal will be sent to the TCC regarding the HSR driver's vigilance level. The TCC will make judgment according to different ...
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Citations
... The results showed that recurrence patterns, which are known to capture the non-linear dynamics of HRV, are reliable predictors of sleepiness. The use of demographic attributes, such as age, height, weight (Sedighi Maman et al., 2020;Wang et al., 2018); contextual information, e.g. sleep quality, circular rhythm, and working conditions (Fu et al., 2016); and work duration and years of work experience (Aryal et al., 2017) combined with physiological and motion parameters, has been shown to improve the performance of fatigue models. ...
Road performance continues to be improved to minimize the occurrence of road accidents. But on roads that have good performance, accidents still often occur. Based on the historical occurrence of accidents on toll roads, 45% of the incidents were caused by drivers suffering from fatigue and drowsiness on the road. Straight and monotonous roads are one of the main causes of highway hypnosis and decreased driver alertness. This is a requirement for the development of safe road design through real-time fatigue monitoring. In general, the researcher developed a fatigue model based on motion (MOT), electroencephalogram (EEG), photoplethysmogram (PPG), electrocardiogram (ECG), galvanic skin response (GSR), electromyogram (EMG), skin temperature (Tsk), eye movement (Eye Movement Data), and respiration (RES) obtained through the device used. Supervised machine learning models, and more specifically binary classification models. These models are considered to have excellent performance in detecting fatigue, yet little effort has been made to ensure the use of high-quality data during model development. However, driving performance and some physiological markers of alertness have not been observed in measured road geometric designs. The impact of monotony on road geometry has not been studied in detail. Also, no studies have been found that apply fatigue avoidance in road geometric design to improve driving alertness that can be used and guided by road designers, landscape architects, and traffic engineers to improve road safety. Together, the findings of this review reveal that methodological limitations have hampered the generalizability and road safety applicability of most of the proposed fatigue models.
... EEG signals over the brain's surface have been recorded at intensities between 0 and 200 µV. 6 The brain's signaling rhythms are typically categorized into bands according to their frequencies. Although the names for these frequency ranges are arbitrary, they are commonly used to imply that the rhythmic activity observed at those frequencies holds biological significance and follows a particular pattern of distribution across the scalp. ...
A machine learning model that operates on raw electroencephalogram (EEG) signals is essential for accurately discerning the user’s current thoughts. Given the difficulty of categorizing EEG signals for use in brain-computer interface (BCI) programs, we adopted a systematic approach in this study to select an optimal predictive model. To enhance the effectiveness of our systematic approach, we extracted features such as band powers, averages, and root-mean-squared values. K-nearest neighbor (KNN), principal component analysis, and dual-layer neural networks were employed to evaluate and validate the effectiveness of the extracted features. The BCI IV competition-I dataset was utilized for analysis and validation. KNN achieved an average classification success rate of 98.02% compared to other methods. Furthermore, our research extends the application of this approach using it to create, test, and evaluate human driving behavior as a case study.
... In [12], researchers used eight EEG channels to monitor drivers' state and then applied a matrix decomposition algorithm to classify the EEG signals collected using wireless wearable technology. If a driver was determined to be fatigued, an early warning alert sounded. ...
... In this comparison of the three methods, all were multi-feature fusion methods for fatigue detection. In reference [12], PERCLOS, eye closure duration, and mouth opening times are used as fatigue detection characteristics, and the fusion algorithm of the fatigue decision-making level is a BP neural network. Due to the relatively small number of features, the accuracy was the lowest among the three. ...
Traffic command and scheduling are the core monitoring aspects of railway transportation. Detecting the fatigued state of dispatchers is, therefore, of great significance to ensure the safety of railway operations. In this paper, we present a multi-feature fatigue detection method based on key points of the human face and body posture. Considering unfavorable factors such as facial occlusion and angle changes that have limited single-feature fatigue state detection methods, we developed our model based on the fusion of body postures and facial features for better accuracy. Using facial key points and eye features, we calculate the percentage of eye closure that accounts for more than 80% of the time duration, as well as blinking and yawning frequency, and we analyze fatigue behaviors, such as yawning, a bowed head (that could indicate sleep state), and lying down on a table, using a behavior recognition algorithm. We fuse five facial features and behavioral postures to comprehensively determine the fatigue state of dispatchers. The results show that on the 300 W dataset, as well as a hand-crafted dataset, the inference time of the improved facial key point detection algorithm based on the retina–face model was 100 ms and that the normalized average error (NME) was 3.58. On our own dataset, the classification accuracy based the an Bi-LSTM-SVM adaptive enhancement algorithm model reached 97%. Video data of volunteers who carried out scheduling operations in the simulation laboratory were used for our experiments, and our multi-feature fusion fatigue detection algorithm showed an accuracy rate of 96.30% and a recall rate of 96.30% in fatigue classification, both of which were higher than those of existing single-feature detection methods. Our multi-feature fatigue detection method offers a potential solution for fatigue level classification in vital areas of the industry, such as in railway transportation.
... The literature reviewed can be grouped by the research topic worked on. Ordered descending by paper count, the papers dealt with Emotion Classification , Drowsiness or Fatigue Detection [88][89][90][91][92][93][94][95][96], Epilleptic Seizure Detection [97][98][99][100][101][102][103][104][105][106], General Mental State Classification [20,[107][108][109][110][111][112][113], Stress Classification [114][115][116][117][118][119][120], Sensor or Robotics Performance Evaluation [121][122][123], Predicting communication efforts [124,125], Workload Classification [126,127], Activity Recognition [128], Color Perception [129], Decision-Making Prediction [130], Distraction Detection [131], Fear Detection [132], Guilt-Knowledge Testing [133], and Evaluation of Athletes [134]. ...
... The authors classification accuracy dropped significantly when using only one modality. [95] worked on driver drowsiness detection in the context of high-speed trains. The respective related work was outperformed when using a time window of 9 s for classification. ...
Background
Wearable multi-modal time-series classification applications outperform their best uni-modal counterparts and hold great promise. A modality that directly measures electrical correlates from the brain is electroencephalography. Due to varying noise sources, different key brain regions, key frequency bands, and signal characteristics like non-stationarity, techniques for data pre-processing and classification algorithms are task-dependent.
Method
Here, a systematic literature review on mental state classification for wearable electroencephalography is presented. Four search terms in different combinations were used for an in-title search. The search was executed on the 29th of June 2022, across Google Scholar, PubMed, IEEEXplore, and ScienceDirect. 76 most relevant publications were set into context as the current state-of-the-art in mental state time-series classification.
Results
Pre-processing techniques, features, and time-series classification models were analyzed. Across publications, a window length of one second was mainly chosen for classification and spectral features were utilized the most. The achieved performance per time-series classification model is analyzed, finding linear discriminant analysis, decision trees, and k-nearest neighbors models outperform support-vector machines by a factor of up to 1.5. A historical analysis depicts future trends while under-reported aspects relevant to practical applications are discussed.
Conclusions
Five main conclusions are given, covering utilization of available area for electrode placement on the head, most often or scarcely utilized features and time-series classification model architectures, baseline reporting practices, as well as explainability and interpretability of Deep Learning. The importance of a ‘test battery’ assessing the influence of data pre-processing and multi-modality on time-series classification performance is emphasized.
... ere are two kinds of methods to deal with the problem of matrix rank minimization: one is to relax the rank function convex into the matrix kernel norm and establish the optimization model of the kernel norm; the second is to give the rank of the matrix in advance and establish a lowrank decomposition model [5]. Many domestic experts and scholars have also carried out a lot of researches on matrix filling and applied the research results to the fields of image processing, text analysis, and recommendation system [6]. ...
The traditional filling method for network big data matrix has poor filling effect and suffers from noise. Therefore, a filling algorithm for network big data high rank matrix based on density peak clustering is proposed. The missing data are replaced by small-interval data, the information entropy of the high rank matrix of network big data is calculated, the density peak clustering algorithm is optimized through the cluster center selection strategy, the block data set is obtained through the unknown block method, and the block filling is realized by the host filling algorithm. Experimental results show that the filling accuracy of the proposed algorithm is as high as 0.895, and the loss rate is between 2% and 12%.
... With the recent development of artificial intelligence technology, many researchers have applied machine learning algorithms to EEG analysis to identify driver fatigue. Using a wireless EEG acquisition device, Zhou et al. [62] collected EEG data from 10 train drivers and tested them on EEG, achieving a 99.4% correct classification rate within a 9-second time window. Zhai et al. [61] proposed a two-layer superimposed ensemble learning model based on EEG signals to estimate the alertness of highway drivers. ...
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.
... Several efforts have been made to develop a reliable automatic CAP-scoring algorithm [20][21][22][23]. Most of these methods rely on the extraction of spectral features from the EEG and on the application of machine-learning algorithms, such as the k-nearest neighbor, support vector machine, artificial neural network, decision trees, and deep neural network [24,25]. However, EEG acquisition needs to be carried out under the guidance of experts, and the wearing of the equipment will also affect the sleep state of the subjects [26]. ...
The cyclic alternating pattern is the periodic electroencephalogram activity occurring during non-rapid eye movement sleep. It is a marker of sleep instability and is correlated with several sleep-related pathologies. Considering the connection between the human heart and brain, our study explores the feasibility of using cardiopulmonary features to automatically detect the cyclic alternating pattern of sleep and hence diagnose sleep-related pathologies. By statistically analyzing and comparing the cardiopulmonary characteristics of a healthy group and groups with sleep-related diseases, an automatic recognition scheme of the cyclic alternating pattern is proposed based on the cardiopulmonary resonance indices. Using the Hidden Markov and Random Forest, the scheme combines the variation and stability of measurements of the coupling state of the cardiopulmonary system during sleep. In this research, the F1 score of the sleep-wake classification reaches 92.0%. In terms of the cyclic alternating pattern, the average recognition rate of A-phase reaches 84.7% on the CAP Sleep Database of 108 cases of people. The F1 score of disease diagnosis is 87.8% for insomnia and 90.0% for narcolepsy.
... Physiological signal-based methods detect the onset of fatigue based on changes in subjects' physiological responses such as brain activity measured by electroencephalogram (EEG), heart rate (HR), or electromyogram (EMG). Electroencephalogram, for example, has been considered the gold standard for vigilance (Zhang et al., 2017;Zhou et al., 2018) and driver drowsiness detection (Hu et al., 2013;Sikander and Anwar, 2019;Hu and Lodewijks, 2020). ...
The objective measurement of fatigue is of critical relevance in areas such as occupational health and safety as fatigue impairs cognitive and motor performance, thus reducing productivity and increasing the risk of injury. Wearable systems represent highly promising solutions for fatigue monitoring as they enable continuous, long-term monitoring of biomedical signals in unattended settings, with the required comfort and non-intrusiveness. This is a p rerequisite for the development of accurate models for fatigue monitoring in real-time. However, monitoring fatigue through wearable devices imposes unique challenges. To provide an overview of the current state-of-the-art in monitoring variables associated with fatigue via wearables and to detect potential gaps and pitfalls in current knowledge, a systematic review was performed. The Scopus and PubMed databases were searched for articles published in English since 2015, having the terms “fatigue,” “drowsiness,” “vigilance,” or “alertness” in the title, and proposing wearable device-based systems for non-invasive fatigue quantification. Of the 612 retrieved articles, 60 satisfied the inclusion criteria. Included studies were mainly of short duration and conducted in laboratory settings. In general, researchers developed fatigue models based on motion (MOT), electroencephalogram (EEG), photoplethysmogram (PPG), electrocardiogram (ECG), galvanic skin response (GSR), electromyogram (EMG), skin temperature (Tsk), eye movement (EYE), and respiratory (RES) data acquired by wearable devices available in the market. Supervised machine learning models, and more specifically, binary classification models, are predominant among the proposed fatigue quantification approaches. These models were considered to perform very well in detecting fatigue, however, little effort was made to ensure the use of high-quality data during model development. Together, the findings of this review reveal that methodological limitations have hindered the generalizability and real-world applicability of most of the proposed fatigue models. Considerably more work is needed to fully explore the potential of wearables for fatigue quantification as well as to better understand the relationship between fatigue and changes in physiological variables.
... It is very important to maintain a high level of vigilance [5,6]. erefore, it is very important to study the changing laws of vigilance, establish an effective vigilance detection system, and then study effective countermeasures to maintain vigilance and even increase vigilance [7,8]. Diseases may occur suddenly, with severe symptoms and rapid changes. ...
In this paper, the pulse wave feature alertness detection system based on computer software technology is researched. First, the computer software technology designs the alertness detection system and then conducts the system alertness test experiment using a system that can not affect the subjects’ alertness, a portable multichannel physiological signal acquisition system that measures the subjects’ ECG signal, skin resistance, blood oxygen saturation, and other physiological signals in the case of a degree task experiment. The multichannel physiological signal acquisition system collects the signals during the vigilance task experiment. At the same time, before, during, and after the experiment, subjects are required to fill in the Stanford Sleepiness Scale (SSS) and evaluate the level of individual alertness through subjective self-evaluation. The relevant experimental data show that, 10 minutes before the experiment, the pulse amplitude increased rapidly, then slowly decreased at the beginning, reached a peak in about 25 minutes, and then began to rise.
... As expected, (mental) fatigue and drowsiness constituted a high fraction of these studies, in particular for different aspects of aircraft piloting (Guo et al., 2018;Hankins & Wilson, 1998;Lin et al., 2014;Rohit et al., 2017, Wilson, 2002. Zhang et al., 2017Zhou et al., 2018), manual assembly processes (Xiao et al., 2018), or in the context of a logistics workplace (Wascher, Heppner, et al., 2014;. These studies included user state examinations, the development of countermeasures to critical aspects of safety in the workplace (Zhou et al., 2018), or a brain-computer interface (BCI)based control of driving speed (Zhang et al., 2016). ...
... Zhang et al., 2017Zhou et al., 2018), manual assembly processes (Xiao et al., 2018), or in the context of a logistics workplace (Wascher, Heppner, et al., 2014;. These studies included user state examinations, the development of countermeasures to critical aspects of safety in the workplace (Zhou et al., 2018), or a brain-computer interface (BCI)based control of driving speed (Zhang et al., 2016). ...
Objective
We demonstrate and discuss the use of mobile electroencephalogram (EEG) for neuroergonomics. Both technical state of the art as well as measures and cognitive concepts are systematically addressed.
Background
Modern work is increasingly characterized by information processing. Therefore, the examination of mental states, mental load, or cognitive processing during work is becoming increasingly important for ergonomics.
Results
Mobile EEG allows to measure mental states and processes under real live conditions. It can be used for various research questions in cognitive neuroergonomics. Besides measures in the frequency domain that have a long tradition in the investigation of mental fatigue, task load, and task engagement, new approaches—like blink-evoked potentials—render event-related analyses of the EEG possible also during unrestricted behavior.
Conclusion
Mobile EEG has become a valuable tool for evaluating mental states and mental processes on a highly objective level during work. The main advantage of this technique is that working environments don’t have to be changed while systematically measuring brain functions at work. Moreover, the workflow is unaffected by such neuroergonomic approaches.
