@article{ref1,
title="Stress influence on real-world driving identified by monitoring heart rate variability and morphologic variability of ECG signals: the case of intercity roads",
journal="International journal of occupational safety and ergonomics",
year="2023",
author="Rostamzadeh, Sajjad and Abouhossein, Alireza and Vosoughi, Shahram and Gendeshmin, Saeid Bahramzadeh and Yarahmadi, Rasoul",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="BACKGROUND: This study examines which one of the heart rate variability (HRV) and morphologic variability (MV) metrics may have the highest accuracy in different stress detection during real-world driving. MATERIAL AND METHODS: This cross-sectional study was carried out among 93 intercity mini-bus male drivers aged 22-67 years. Trillium 5000 Holter Recorder and GARMIN Virb Elite camera were used to determine heart rate and vehicle speed measurements along the path, respectively. We have considered the HRV and MV metrics of ECG signals including mean RR interval (mRR), mean heart rate (mHR), normalized low-frequency spectrum (nLF), normalized high-frequency spectrum (nHF), normalized very low-frequency spectrum (nVLF), a difference of normalized low-frequency spectrum and normalized high-frequency spectrum (dLFHF), and sympathovagal balance index (SVI). <br><br>RESULTS: The analysis showed that HRV metrics named mHR, mRR, nVLF, nLF, nHF, dLFHF, and SVI are effective in mental stress detection while driving as compared to rest time. We obtained a high accuracy of stress detection for MV metrics as compared to the traditional HRV analysis, approximately 92%. <br><br>CONCLUSIONS: Our findings indicate that driver stress could be detected with an accuracy of 92% using MV metrics as an accurate physiological index of the driver's state.<p /> <p>Language: en</p>",
language="en",
issn="1080-3548",
doi="10.1080/10803548.2023.2293391",
url="http://dx.doi.org/10.1080/10803548.2023.2293391"
}