College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China.
Hami Vocational and Technical College, Hami 839001, China.
Sensors (Basel). 2024 Jul 3;24(13):4316. doi: 10.3390/s24134316.
Traffic accidents due to fatigue account for a large proportion of road fatalities. Based on simulated driving experiments with drivers recruited from college students, this paper investigates the use of heart rate variability (HRV) features to detect driver fatigue while considering sex differences. Sex-independent and sex-specific differences in HRV features between alert and fatigued states derived from 2 min electrocardiogram (ECG) signals were determined. Then, decision trees were used for driver fatigue detection using the HRV features of either all subjects or those of only males or females. Nineteen, eighteen, and thirteen HRV features were significantly different (Mann-Whitney U test, < 0.01) between the two mental states for all subjects, males, and females, respectively. The fatigue detection models for all subjects, males, and females achieved classification accuracies of 86.3%, 94.8%, and 92.0%, respectively. In conclusion, sex differences in HRV features between drivers' mental states were found according to both the statistical analysis and classification results. By considering sex differences, precise HRV feature-based driver fatigue detection systems can be developed. Moreover, in contrast to conventional methods using HRV features from 5 min ECG signals, our method uses HRV features from 2 min ECG signals, thus enabling more rapid driver fatigue detection.
交通事故导致的疲劳占道路死亡人数的很大比例。本研究通过从大学生中招募驾驶员进行模拟驾驶实验，调查了心率变异性（HRV）特征在考虑性别差异时检测驾驶员疲劳的应用。确定了从 2 分钟心电图（ECG）信号中得出的警觉和疲劳状态下 HRV 特征的性别独立和性别特异性差异。然后，使用决策树根据所有受试者、仅男性或仅女性的 HRV 特征进行驾驶员疲劳检测。对于所有受试者、男性和女性，分别有 19、18 和 13 个 HRV 特征在两种心理状态之间存在显著差异（Mann-Whitney U 检验， < 0.01）。对于所有受试者、男性和女性的疲劳检测模型，分类准确率分别为 86.3%、94.8%和 92.0%。总之，根据统计分析和分类结果，发现了驾驶员心理状态下 HRV 特征的性别差异。通过考虑性别差异，可以开发出精确的基于 HRV 特征的驾驶员疲劳检测系统。此外，与使用 5 分钟 ECG 信号的 HRV 特征的传统方法相比，我们的方法使用 2 分钟 ECG 信号的 HRV 特征，从而能够更快速地检测驾驶员疲劳。
