@article{ref1,
title="Driver stress detection using ultra-short-term HRV analysis under real world driving conditions",
journal="Entropy (Basel, Switzerland)",
year="2023",
author="Liu, Kun and Jiao, Yubo and Du, Congcong and Zhang, Xiaoming and Chen, Xiaoyu and Xu, Fang and Jiang, Chaozhe",
volume="25",
number="2",
pages="e194-e194",
abstract="Considering that driving stress is a major contributor to traffic accidents, detecting drivers' stress levels in time is helpful for ensuring driving safety. This paper attempts to investigate the ability of ultra-short-term (30-s, 1-min, 2-min, and 3-min) HRV analysis for driver stress detection under real driving circumstances. Specifically, the t-test was used to investigate whether there were significant differences in HRV features under different stress levels. Ultra-short-term HRV features were compared with the corresponding short-term (5-min) features during low-stress and high-stress phases by the Spearman rank correlation and Bland-Altman plots analysis. Furthermore, four different machine-learning classifiers, including a support vector machine (SVM), random forests (RFs), K-nearest neighbor (KNN), and Adaboost, were evaluated for stress detection. The results show that the HRV features extracted from ultra-short-term epochs were able to detect binary drivers' stress levels accurately. In particular, although the capability of HRV features in detecting driver stress also varied between different ultra-short-term epochs, MeanNN, SDNN, NN20, and MeanHR were selected as valid surrogates of short-term features for driver stress detection across the different epochs. For drivers' stress levels classification, the best performance was achieved with the SVM classifier, with an accuracy of 85.3% using 3-min HRV features. This study makes a contribution to building a robust and effective stress detection system using ultra-short-term HRV features under actual driving environments.<p /> <p>Language: en</p>",
language="en",
issn="1099-4300",
doi="10.3390/e25020194",
url="http://dx.doi.org/10.3390/e25020194"
}