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http://hdl.handle.net/10397/105797
Title: | Driver stress detection using ultra-short-term HRV analysis under real world driving conditions | Authors: | Liu, K Jiao, Y Du, C Zhang, X Chen, X Xu, F Jiang, C |
Issue Date: | Feb-2023 | Source: | Entropy, Feb. 2023, v. 25, no. 2, 194 | 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. | Keywords: | Classification Driving safety Heart rate variability Machine learning Stress detection |
Publisher: | MDPI AG | Journal: | Entropy | EISSN: | 1099-4300 | DOI: | 10.3390/e25020194 | Rights: | Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). The following publication Liu K, Jiao Y, Du C, Zhang X, Chen X, Xu F, Jiang C. Driver Stress Detection Using Ultra-Short-Term HRV Analysis under Real World Driving Conditions. Entropy. 2023; 25(2):194 is available at https://doi.org/10.3390/e25020194. |
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entropy-25-00194.pdf | 1.37 MB | Adobe PDF | View/Open |
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