Real-Time Embedded Vision System for the Watchfulness Analysis of Train Drivers

Avizzano, Carlo Alberto; Tripicchio, Paolo; Ruffaldi, Emanuele; Filippeschi, Alessandro; Jacinto‐Villegas, Juan Manuel

doi:10.1109/tits.2019.2955787

Cited by 8 publications

(3 citation statements)

References 26 publications

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“…To determine whether the changes in 20 HRV features are similar to the findings in previous studies, a two-way analysis of variance (ANOVA) was conducted using fatigue (KSS score ł 5, 6 ł KSS score ł 7, and 8 ł KSS score) and participant (1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17) as two factors. To investigate whether the normalization could eliminate the large inter-individual differences in HRV features, a two-way ANOVA was carried out for both non-normalized and normalized HRV features.…”

Section: Discussionmentioning

confidence: 91%

“…Previous studies have made some progress in driver fatigue detection. The methods used can be divided into three categories: (a) vehicle-based methods such as steering angle and lane deviation; (b) behavioral-based methods such as yawning, blink rate ( 7 ), and percentage of eye open; and (c) physiological-based methods such as electroencephalography (EEG) ( 8 ), electrocardiogram (ECG) ( 9 , 10 ), and electrooculography (EOG). However, because the longitudinal and lateral positioning of URT is secured, vehicle-based methods cannot be used for driver fatigue detection.…”

mentioning

confidence: 99%

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Label-Less Learning for Urban Railway Transit Driver Fatigue Detection with Heart Rate Variability

Jiao

Tan

Zhang

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Driver fatigue has a direct impact on urban railway transit (URT) drivers’ driving behavior and can cause driver error. The existing methods for fatigue detection mainly train the models with supervised learning, relying heavily on the annotation of recorded data. However, labeled data are unobtainable in some environments, especially for URT driver fatigue levels during actual driving. Therefore, this study proposes a fatigue detection method using unlabeled heart rate variability data to monitor URT driver fatigue in actual working conditions. By utilizing the existing conclusions with regard to factors contributing to fatigue and physiological changes, this study annotated a small number of samples and then used a novel positive and unlabeled learning algorithm based on nearest neighbors and random forest to divide samples into different fatigue levels. The proposed method was evaluated using the URT driver fatigue data sets collected in the lab. Binary classification achieved an accuracy of 79.0%. However, the accuracy of three-class classification was only 55.7%. In addition, the proposed method performed as well using the field data set as it did using the lab data set. The results show the high generalization performance of the proposed method, which could contribute to addressing the issue of lack of labeled training data for fatigue detection in actual working conditions.

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Section: Discussionmentioning

confidence: 91%

mentioning

confidence: 99%

Label-Less Learning for Urban Railway Transit Driver Fatigue Detection with Heart Rate Variability

Jiao

Tan

Zhang

et al. 2022

Transportation Research Record: Journal of the Transportation R

View full text Add to dashboard Cite

show abstract

“…Driver fatigue detection systems may reduce such accidents and incidents by establishing fitness-for-duty requirements that could significantly improve safety. Such systems can be divided into four categories: (i) subjective reporting methods such as Karolinska Sleepiness Scale; (ii) biological features-based methods such as electroencephalography or electrocardiogram (ECG); electrooculography and electromyogram; (iii) physical features-based methods such as blink frequency and eye closure duration; and (iv) vehicular features-based methods such as steering wheel angle and lane deviation (7)(8)(9)(10).…”

mentioning

confidence: 99%

Data-Driven Detection and Assessment for Urban Railway Transit Driver Fatigue in Real Work Conditions

Jiao

Chen

Sun

et al. 2022

Transportation Research Record: Journal of the Transportation R

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Fatigue among urban railway transit (URT) drivers affects their performance and is a contributing factor in many railway accidents and incidents. This paper attempts to develop a robust fatigue detection system for URT drivers. An experimental study was conducted in actual work conditions, involving 198 professional URT drivers, to provide authentic and representative data. Fatigue scores based on the Karolinska Sleepiness Scale were used as the ground truth, and heart rate variability (HRV) data were collected using wearable photoplethysmography (PPG) sensors under actual working conditions. An extensive statistical analysis found that continuous working hours were a major factor in driver fatigue. HRV features were able to differentiate various fatigue levels. Four classifiers (k-nearest neighbors, Naive Bayes, support vector machines, and random forests) were trained to detect fatigue in real time for binary and three-class fatigue classifications, respectively. For the binary classification, the best performance was achieved by the random forest classifier using the corrected feature set as input with an accuracy of 92.5%. However, the accuracy dropped by 8 to 27 percentage points for the three-class classification. Moreover, the research found that the corrected feature set circumventing inter-individual variability in HRV could improve the performance of fatigue classifiers. The findings from this research could contribute to developing a robust and real-time URT driver fatigue detection system and improve current URT operational safety regulations.

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Physiological responses and stress levels of high-speed rail train drivers under various operating conditions - a simulator study in China

Jiao

Sun

et al. 2022

International Journal of Rail Transportation

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Real-Time Embedded Vision System for the Watchfulness Analysis of Train Drivers

Cited by 8 publications

References 26 publications

Label-Less Learning for Urban Railway Transit Driver Fatigue Detection with Heart Rate Variability

Label-Less Learning for Urban Railway Transit Driver Fatigue Detection with Heart Rate Variability

Data-Driven Detection and Assessment for Urban Railway Transit Driver Fatigue in Real Work Conditions

Physiological responses and stress levels of high-speed rail train drivers under various operating conditions - a simulator study in China

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