TY - JOUR
PY - 2023//
TI - Driver stress detection using ultra-short-term HRV analysis under real world driving conditions
JO - Entropy (Basel, Switzerland)
A1 - Liu, Kun
A1 - Jiao, Yubo
A1 - Du, Congcong
A1 - Zhang, Xiaoming
A1 - Chen, Xiaoyu
A1 - Xu, Fang
A1 - Jiang, Chaozhe
SP - e194
EP - e194
VL - 25
IS - 2
N2 - 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. Language: en
LA - en
SN - 1099-4300
UR - http://dx.doi.org/10.3390/e25020194
ID - ref1
ER -