Discrimination of Driver Fatigue Based on Distortion Energy Density Theory and Multiple Physiological Signals

Driver fatigue is an important contributor to traffic accidents, and driver fatigue is significant for the safety of people’s lives. Aiming to prevent traffic accidents caused by driver fatigue, a series of real driving experiments was carried out in the present work. First, based on an a...

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Autores principales:	Lin Wang, Hong Wang, Jintao Liu
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Discriminant model of driver fatigue distortion energy density (DED) theory multiple physiological signals approximate entropy Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/cee64c140f684c8f81dce2d4bdc128dd
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id	oai:doaj.org-article:cee64c140f684c8f81dce2d4bdc128dd
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spelling	oai:doaj.org-article:cee64c140f684c8f81dce2d4bdc128dd2021-11-17T00:00:54ZDiscrimination of Driver Fatigue Based on Distortion Energy Density Theory and Multiple Physiological Signals2169-353610.1109/ACCESS.2021.3125052https://doaj.org/article/cee64c140f684c8f81dce2d4bdc128dd2021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9599716/https://doaj.org/toc/2169-3536Driver fatigue is an important contributor to traffic accidents, and driver fatigue is significant for the safety of people’s lives. Aiming to prevent traffic accidents caused by driver fatigue, a series of real driving experiments was carried out in the present work. First, based on an analysis with respect to distortion energy density (DED) theory and the experimental results, the upper trapezius at 6<sup>th</sup> neck vertebrae is more sensitive to driver fatigue and easier to fatigue than that at 7<sup>th</sup> neck vertebrae in a real driving. And then 2 cm from the 6<sup>th</sup> vertebrae on both sides were selected as the locations of data acquisition for electromyography (EMG) signal. The experimental results show that the approximate entropy (ApEn) from the electroencephalography (EEG), EMG, and respiration (RESP) signals decreases with increasing driving time, indicating that the degree of fatigue increases. After approximately 90 min, the rate of decrease in ApEn becomes slow, indicating deeper driver fatigue. According to three-D analysis, principal component analysis, and fuzzy C-means clustering analysis, the EEG-EMG combination effectively reflects the state of drivers. Finally, the ApEns from EEG and EMG were selected as independent variables, and a discriminant model of driver fatigue based on Mahalanobis distance theory was built. The accuracy of the model is up to 90.92% by 10-fold cross validation. The reasons for the high accuracy are the reasonable selection of the locations of EMG data acquisition and better degree of discrimination of EEG and EMG. The main contributions of this study are to provide a theoretical foundation for establishing internationally recognized standard locations for neck EMG data acquisition, and to provide a feasible method for discriminating driver fatigue in real driving tasks.Lin WangHong WangJintao LiuIEEEarticleDiscriminant model of driver fatiguedistortion energy density (DED) theorymultiple physiological signalsapproximate entropyElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 151824-151833 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Discriminant model of driver fatigue distortion energy density (DED) theory multiple physiological signals approximate entropy Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Discriminant model of driver fatigue distortion energy density (DED) theory multiple physiological signals approximate entropy Electrical engineering. Electronics. Nuclear engineering TK1-9971 Lin Wang Hong Wang Jintao Liu Discrimination of Driver Fatigue Based on Distortion Energy Density Theory and Multiple Physiological Signals
description	Driver fatigue is an important contributor to traffic accidents, and driver fatigue is significant for the safety of people’s lives. Aiming to prevent traffic accidents caused by driver fatigue, a series of real driving experiments was carried out in the present work. First, based on an analysis with respect to distortion energy density (DED) theory and the experimental results, the upper trapezius at 6<sup>th</sup> neck vertebrae is more sensitive to driver fatigue and easier to fatigue than that at 7<sup>th</sup> neck vertebrae in a real driving. And then 2 cm from the 6<sup>th</sup> vertebrae on both sides were selected as the locations of data acquisition for electromyography (EMG) signal. The experimental results show that the approximate entropy (ApEn) from the electroencephalography (EEG), EMG, and respiration (RESP) signals decreases with increasing driving time, indicating that the degree of fatigue increases. After approximately 90 min, the rate of decrease in ApEn becomes slow, indicating deeper driver fatigue. According to three-D analysis, principal component analysis, and fuzzy C-means clustering analysis, the EEG-EMG combination effectively reflects the state of drivers. Finally, the ApEns from EEG and EMG were selected as independent variables, and a discriminant model of driver fatigue based on Mahalanobis distance theory was built. The accuracy of the model is up to 90.92% by 10-fold cross validation. The reasons for the high accuracy are the reasonable selection of the locations of EMG data acquisition and better degree of discrimination of EEG and EMG. The main contributions of this study are to provide a theoretical foundation for establishing internationally recognized standard locations for neck EMG data acquisition, and to provide a feasible method for discriminating driver fatigue in real driving tasks.
format	article
author	Lin Wang Hong Wang Jintao Liu
author_facet	Lin Wang Hong Wang Jintao Liu
author_sort	Lin Wang
title	Discrimination of Driver Fatigue Based on Distortion Energy Density Theory and Multiple Physiological Signals
title_short	Discrimination of Driver Fatigue Based on Distortion Energy Density Theory and Multiple Physiological Signals
title_full	Discrimination of Driver Fatigue Based on Distortion Energy Density Theory and Multiple Physiological Signals
title_fullStr	Discrimination of Driver Fatigue Based on Distortion Energy Density Theory and Multiple Physiological Signals
title_full_unstemmed	Discrimination of Driver Fatigue Based on Distortion Energy Density Theory and Multiple Physiological Signals
title_sort	discrimination of driver fatigue based on distortion energy density theory and multiple physiological signals
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/cee64c140f684c8f81dce2d4bdc128dd
work_keys_str_mv	AT linwang discriminationofdriverfatiguebasedondistortionenergydensitytheoryandmultiplephysiologicalsignals AT hongwang discriminationofdriverfatiguebasedondistortionenergydensitytheoryandmultiplephysiologicalsignals AT jintaoliu discriminationofdriverfatiguebasedondistortionenergydensitytheoryandmultiplephysiologicalsignals
_version_	1718426075197865984

Discrimination of Driver Fatigue Based on Distortion Energy Density Theory and Multiple Physiological Signals

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