{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T07:31:23Z","timestamp":1772523083504,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T00:00:00Z","timestamp":1674086400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan Social Science Key Research Base National Park Research Center","award":["GJGY2022-YB009"],"award-info":[{"award-number":["GJGY2022-YB009"]}]},{"name":"Sichuan Social Science Key Research Base National Park Research Center","award":["FZ2021KF05"],"award-info":[{"award-number":["FZ2021KF05"]}]},{"name":"Key Laboratory of Flight Techniques and Flight Safety","award":["GJGY2022-YB009"],"award-info":[{"award-number":["GJGY2022-YB009"]}]},{"name":"Key Laboratory of Flight Techniques and Flight Safety","award":["FZ2021KF05"],"award-info":[{"award-number":["FZ2021KF05"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Considering that driving stress is a major contributor to traffic accidents, detecting drivers\u2019 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\u2013Altman 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\u2019 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\u2019 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.<\/jats:p>","DOI":"10.3390\/e25020194","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T02:51:37Z","timestamp":1674096697000},"page":"194","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Driver Stress Detection Using Ultra-Short-Term HRV Analysis under Real World Driving Conditions"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8170-0310","authenticated-orcid":false,"given":"Kun","family":"Liu","sequence":"first","affiliation":[{"name":"School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 610097, China"}]},{"given":"Yubo","family":"Jiao","sequence":"additional","affiliation":[{"name":"School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 610097, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-1473-5259","authenticated-orcid":false,"given":"Congcong","family":"Du","sequence":"additional","affiliation":[{"name":"School of Mines, China University of Mining and Technology, Xuzhou 221116, China"},{"name":"Department of Aeronautical and Aviation Engineering, The Hong Kong Polytechnic University, Hung Hom, Hong Kong, China"}]},{"given":"Xiaoming","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 610097, China"}]},{"given":"Xiaoyu","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 610097, China"}]},{"given":"Fang","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Purchase Management, Sichuan Tourism University, Chengdu 610100, China"}]},{"given":"Chaozhe","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Transportation & Logistics, Southwest Jiaotong University, Chengdu 610097, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITS.2005.848368","article-title":"Detecting Stress during Real-World Driving Tasks Using Physiological Sensors","volume":"6","author":"Healey","year":"2005","journal-title":"IEEE Trans. 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