Drivers Pressures States Recognition based on Heart Rate Variability

Kongjian Qin, Hongwei Liu, Mingjun Zhang, Jinchong Zhang

2021

Abstract

Drivers pressures are major causes of road accidents, and thus drivers’ pressures states recognition become an important topic in Advanced Driver Assistant System (ADAS). Physiological signals provide information about the internal functioning of human body and thereby provide accurate, reliable and robust information on the driver’s state. In this work, the several features, which are 8 heart rate variability features and 10 mathematical features, are trained using three classifiers: Support Vector Machine (SVM), K-nearest-neighbor (KNN) and Ensemble. The algorithms based pNN5 and LF/HF achieved best performance in HRV linear features evaluation, and the accuracy (AC), sensitivity (SE), specificity (SP) for Stress Recognition in Automobile Drivers data are 89.0%, 91.8% and 77.3% respectively. The mathematical features result in 98.6%,99.1% and 91.5% for accuracy (AC), sensitivity (SE), specificity, respectively.

Paper Citation

in Harvard Style

Qin K., Liu H., Zhang M. and Zhang J. (2021). Drivers Pressures States Recognition based on Heart Rate Variability. In Proceedings of the 2nd Conference on Artificial Intelligence and Healthcare - Volume 1: CAIH, ISBN 978-989-758-594-4, pages 29-33. DOI: 10.5220/0011158800003444

in Bibtex Style

@conference{caih21,
author={Kongjian Qin and Hongwei Liu and Mingjun Zhang and Jinchong Zhang},
title={Drivers Pressures States Recognition based on Heart Rate Variability},
booktitle={Proceedings of the 2nd Conference on Artificial Intelligence and Healthcare - Volume 1: CAIH,},
year={2021},
pages={29-33},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011158800003444},
isbn={978-989-758-594-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 2nd Conference on Artificial Intelligence and Healthcare - Volume 1: CAIH,
TI - Drivers Pressures States Recognition based on Heart Rate Variability
SN - 978-989-758-594-4
AU - Qin K.
AU - Liu H.
AU - Zhang M.
AU - Zhang J.
PY - 2021
SP - 29
EP - 33
DO - 10.5220/0011158800003444