Driver Stress Detection Using Ultra-Short-Term HRV Analysis under Real World Driving Conditions

• Published in: Entropy (Basel, Switzerland) Vol. 25; no. 2; p. 194
• Main Authors: Liu, Kun, Jiao, Yubo, Du, Congcong, Zhang, Xiaoming, Chen, Xiaoyu, Xu, Fang, Jiang, Chaozhe
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 19.01.2023; MDPI
• Subjects: Automobile driving; classification; Classifiers; Correlation analysis; Digital cameras; Driving conditions; driving safety; Experiments; Feature extraction; Heart beat; heart rate variability; Machine learning; Measurement; Motor vehicle driving; Physiology; Psychological aspects; Questionnaires; Statistical analysis; Stress; Stress (Psychology); stress detection; Support vector machines; Traffic; Traffic accidents; Vehicle safety; China; classification; machine learning; driving safety; heart rate variability; stress detection
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e25020194

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 -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.