Driver Fatigue Detection Using Heart Rate Variability Features from 2-Minute Electrocardiogram Signals While Accounting for Sex Differences
Author:
Zeng Chao12, Zhang Jiliang1, Su Yizi3ORCID, Li Shuguang4, Wang Zhenyuan3, Li Qingkun5, Wang Wenjun3
Affiliation:
1. College of Information Science and Engineering, Henan University of Technology, Zhengzhou 450001, China 2. Hami Vocational and Technical College, Hami 839001, China 3. School of Vehicle and Mobility, Tsinghua University, Beijing 100084, China 4. School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China 5. Beijing Key Laboratory of Human-Computer Interaction, Institute of Software, Chinese Academy of Sciences, Beijing 100190, China
Abstract
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, p < 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.
Funder
Key R&D Projects of the Science and Technology Department of China Natural Science Foundation of Xinjiang Uygur Autonomous Region Guangxi Youth Science and Technology innovation talent training program Research Foundation for Talented Scholars of Henan University of Technology
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