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A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals

Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is...

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Autores principales: Liu, Li, Ji, Yunfeng, Gao, Yun, Ping, Zhenyu, Kuang, Liang, Li, Tao, Xu, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8629631/
https://www.ncbi.nlm.nih.gov/pubmed/34853672
http://dx.doi.org/10.1155/2021/7799793
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author Liu, Li
Ji, Yunfeng
Gao, Yun
Ping, Zhenyu
Kuang, Liang
Li, Tao
Xu, Wei
author_facet Liu, Li
Ji, Yunfeng
Gao, Yun
Ping, Zhenyu
Kuang, Liang
Li, Tao
Xu, Wei
author_sort Liu, Li
collection PubMed
description Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background.
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spelling pubmed-86296312021-11-30 A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals Liu, Li Ji, Yunfeng Gao, Yun Ping, Zhenyu Kuang, Liang Li, Tao Xu, Wei J Healthc Eng Research Article Traffic accidents are easily caused by tired driving. If the fatigue state of the driver can be identified in time and a corresponding early warning can be provided, then the occurrence of traffic accidents could be avoided to a large extent. At present, the recognition of fatigue driving states is mostly based on recognition accuracy. Fatigue state is currently recognized by combining different features, such as facial expressions, electroencephalogram (EEG) signals, yawning, and the percentage of eyelid closure over the pupil over time (PERCLoS). The combination of these features increases the recognition time and lacks real-time performance. In addition, some features will increase error in the recognition result, such as yawning frequently with the onset of a cold or frequent blinking with dry eyes. On the premise of ensuring the recognition accuracy and improving the realistic feasibility and real-time recognition performance of fatigue driving states, a fast support vector machine (FSVM) algorithm based on EEGs and electrooculograms (EOGs) is proposed to recognize fatigue driving states. First, the collected EEG and EOG modal data are preprocessed. Second, multiple features are extracted from the preprocessed EEGs and EOGs. Finally, FSVM is used to classify and recognize the data features to obtain the recognition result of the fatigue state. Based on the recognition results, this paper designs a fatigue driving early warning system based on Internet of Things (IoT) technology. When the driver shows symptoms of fatigue, the system not only sends a warning signal to the driver but also informs other nearby vehicles using this system through IoT technology and manages the operation background. Hindawi 2021-11-22 /pmc/articles/PMC8629631/ /pubmed/34853672 http://dx.doi.org/10.1155/2021/7799793 Text en Copyright © 2021 Li Liu et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Liu, Li
Ji, Yunfeng
Gao, Yun
Ping, Zhenyu
Kuang, Liang
Li, Tao
Xu, Wei
A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals
title A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals
title_full A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals
title_fullStr A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals
title_full_unstemmed A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals
title_short A Novel Fatigue Driving State Recognition and Warning Method Based on EEG and EOG Signals
title_sort novel fatigue driving state recognition and warning method based on eeg and eog signals
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8629631/
https://www.ncbi.nlm.nih.gov/pubmed/34853672
http://dx.doi.org/10.1155/2021/7799793
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