Driving Stress Estimation in Physiological Signals Based on Hierarchical Clustering and Multi-View Intact Space Learning

Runqiang, Jiang; Chen, Lan-lan

doi:10.1109/tits.2021.3120435

Cited by 5 publications

(1 citation statement)

References 52 publications

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“…On the one hand, Coutts et al ( 27 ) found that data preprocessing methods taking into account the characteristics of the participants may lead to more accurate results. On the other hand, separate machine learning models built with the data from groups of participants who have similar HRV features or heart rate signals may result in improved classification performance ( 30 ). Moreover, the fuzzy rule should be further improved for accurate fatigue detection.…”

Section: Discussionmentioning

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

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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: 99%