Driver Emotion and Fatigue State Detection Based on Time Series Fusion
Studies have shown that driver fatigue or unpleasant emotions significantly increase driving risks. Detecting driver emotions and fatigue states and providing timely warnings can effectively minimize the incidence of traffic accidents. However, existing models rarely combine driver emotion and fatig...
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Veröffentlicht in: | Electronics (Basel) 2023-01, Vol.12 (1), p.26 |
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description | Studies have shown that driver fatigue or unpleasant emotions significantly increase driving risks. Detecting driver emotions and fatigue states and providing timely warnings can effectively minimize the incidence of traffic accidents. However, existing models rarely combine driver emotion and fatigue detection, and there is space to improve the accuracy of recognition. In this paper, we propose a non-invasive and efficient detection method for driver fatigue and emotional state, which is the first time to combine them in the detection of driver state. Firstly, the captured video image sequences are preprocessed, and Dlib (image open source processing library) is used to locate face regions and mark key points; secondly, facial features are extracted, and fatigue indicators, such as driver eye closure time (PERCLOS) and yawn frequency are calculated using the dual-threshold method and fused by mathematical methods; thirdly, an improved lightweight RM-Xception convolutional neural network is introduced to identify the driver’s emotional state; finally, the two indicators are fused based on time series to obtain a comprehensive score for evaluating the driver’s state. The results show that the fatigue detection algorithm proposed in this paper has high accuracy, and the accuracy of the emotion recognition network reaches an accuracy rate of 73.32% on the Fer2013 dataset. The composite score calculated based on time series fusion can comprehensively and accurately reflect the driver state in different environments and make a contribution to future research in the field of assisted safe driving. |
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Detecting driver emotions and fatigue states and providing timely warnings can effectively minimize the incidence of traffic accidents. However, existing models rarely combine driver emotion and fatigue detection, and there is space to improve the accuracy of recognition. In this paper, we propose a non-invasive and efficient detection method for driver fatigue and emotional state, which is the first time to combine them in the detection of driver state. Firstly, the captured video image sequences are preprocessed, and Dlib (image open source processing library) is used to locate face regions and mark key points; secondly, facial features are extracted, and fatigue indicators, such as driver eye closure time (PERCLOS) and yawn frequency are calculated using the dual-threshold method and fused by mathematical methods; thirdly, an improved lightweight RM-Xception convolutional neural network is introduced to identify the driver’s emotional state; finally, the two indicators are fused based on time series to obtain a comprehensive score for evaluating the driver’s state. The results show that the fatigue detection algorithm proposed in this paper has high accuracy, and the accuracy of the emotion recognition network reaches an accuracy rate of 73.32% on the Fer2013 dataset. The composite score calculated based on time series fusion can comprehensively and accurately reflect the driver state in different environments and make a contribution to future research in the field of assisted safe driving.</description><identifier>ISSN: 2079-9292</identifier><identifier>EISSN: 2079-9292</identifier><identifier>DOI: 10.3390/electronics12010026</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Algorithms ; Artificial neural networks ; Automobile drivers ; Classification ; Datasets ; Driver fatigue ; Electroencephalography ; Emotion recognition ; Emotional factors ; Emotions ; Fatigue ; Feature extraction ; Image processing ; Indicators ; Methods ; Neural networks ; Object recognition (Computers) ; Pattern recognition ; Physiology ; Psychological aspects ; Sequences ; Time series ; Traffic accidents ; Traffic models ; Traffic safety ; Wavelet transforms</subject><ispartof>Electronics (Basel), 2023-01, Vol.12 (1), p.26</ispartof><rights>COPYRIGHT 2022 MDPI AG</rights><rights>2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c389t-2ac8234b1777db53c6a18a3cfbcca6161cc010ff46305bc77234232d13477b213</citedby><cites>FETCH-LOGICAL-c389t-2ac8234b1777db53c6a18a3cfbcca6161cc010ff46305bc77234232d13477b213</cites><orcidid>0000-0003-3904-6324 ; 0000-0002-7335-1587</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Shang, Yucheng</creatorcontrib><creatorcontrib>Yang, Mutian</creatorcontrib><creatorcontrib>Cui, Jianwei</creatorcontrib><creatorcontrib>Cui, Linwei</creatorcontrib><creatorcontrib>Huang, Zizheng</creatorcontrib><creatorcontrib>Li, Xiang</creatorcontrib><title>Driver Emotion and Fatigue State Detection Based on Time Series Fusion</title><title>Electronics (Basel)</title><description>Studies have shown that driver fatigue or unpleasant emotions significantly increase driving risks. Detecting driver emotions and fatigue states and providing timely warnings can effectively minimize the incidence of traffic accidents. However, existing models rarely combine driver emotion and fatigue detection, and there is space to improve the accuracy of recognition. In this paper, we propose a non-invasive and efficient detection method for driver fatigue and emotional state, which is the first time to combine them in the detection of driver state. Firstly, the captured video image sequences are preprocessed, and Dlib (image open source processing library) is used to locate face regions and mark key points; secondly, facial features are extracted, and fatigue indicators, such as driver eye closure time (PERCLOS) and yawn frequency are calculated using the dual-threshold method and fused by mathematical methods; thirdly, an improved lightweight RM-Xception convolutional neural network is introduced to identify the driver’s emotional state; finally, the two indicators are fused based on time series to obtain a comprehensive score for evaluating the driver’s state. The results show that the fatigue detection algorithm proposed in this paper has high accuracy, and the accuracy of the emotion recognition network reaches an accuracy rate of 73.32% on the Fer2013 dataset. The composite score calculated based on time series fusion can comprehensively and accurately reflect the driver state in different environments and make a contribution to future research in the field of assisted safe driving.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Artificial neural networks</subject><subject>Automobile drivers</subject><subject>Classification</subject><subject>Datasets</subject><subject>Driver fatigue</subject><subject>Electroencephalography</subject><subject>Emotion recognition</subject><subject>Emotional factors</subject><subject>Emotions</subject><subject>Fatigue</subject><subject>Feature extraction</subject><subject>Image processing</subject><subject>Indicators</subject><subject>Methods</subject><subject>Neural networks</subject><subject>Object recognition (Computers)</subject><subject>Pattern recognition</subject><subject>Physiology</subject><subject>Psychological aspects</subject><subject>Sequences</subject><subject>Time series</subject><subject>Traffic accidents</subject><subject>Traffic models</subject><subject>Traffic safety</subject><subject>Wavelet transforms</subject><issn>2079-9292</issn><issn>2079-9292</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><recordid>eNptUU1LAzEQDaJg0f4CLwuet-Zjd7M51rarQsGD9Ryys0lJ6W5qkhX896ZUUKEzh3nMezPD8BC6I3jGmMAPeq8hejdYCIRigjGtLtCEYi5yQQW9_IOv0TSEHU4hCKsZnqBm6e2n9tmqd9G6IVNDlzUq2u2os7eoos6WOqb9R-5RBd1lCWxsn1jtrQ5ZM4bE3aIro_ZBT3_qDXpvVpvFc75-fXpZzNc5sFrEnCqoKStawjnv2pJBpUitGJgWQFWkIgDpAWOKiuGyBc6TmDLaEVZw3lLCbtD9ae_Bu49Rhyh3bvRDOikprwghtBblr2qr9lrawbjoFfQ2gJzzoixFXZQ8qWZnVCk73VtwgzY29f8NsNMAeBeC10YevO2V_5IEy6MV8owV7BsIeHwE</recordid><startdate>20230101</startdate><enddate>20230101</enddate><creator>Shang, Yucheng</creator><creator>Yang, Mutian</creator><creator>Cui, Jianwei</creator><creator>Cui, Linwei</creator><creator>Huang, Zizheng</creator><creator>Li, Xiang</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SP</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>HCIFZ</scope><scope>L7M</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><orcidid>https://orcid.org/0000-0003-3904-6324</orcidid><orcidid>https://orcid.org/0000-0002-7335-1587</orcidid></search><sort><creationdate>20230101</creationdate><title>Driver Emotion and Fatigue State Detection Based on Time Series Fusion</title><author>Shang, Yucheng ; Yang, Mutian ; Cui, Jianwei ; Cui, Linwei ; Huang, Zizheng ; Li, Xiang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c389t-2ac8234b1777db53c6a18a3cfbcca6161cc010ff46305bc77234232d13477b213</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Artificial neural networks</topic><topic>Automobile drivers</topic><topic>Classification</topic><topic>Datasets</topic><topic>Driver fatigue</topic><topic>Electroencephalography</topic><topic>Emotion recognition</topic><topic>Emotional factors</topic><topic>Emotions</topic><topic>Fatigue</topic><topic>Feature extraction</topic><topic>Image processing</topic><topic>Indicators</topic><topic>Methods</topic><topic>Neural networks</topic><topic>Object recognition (Computers)</topic><topic>Pattern recognition</topic><topic>Physiology</topic><topic>Psychological aspects</topic><topic>Sequences</topic><topic>Time series</topic><topic>Traffic accidents</topic><topic>Traffic models</topic><topic>Traffic safety</topic><topic>Wavelet transforms</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shang, Yucheng</creatorcontrib><creatorcontrib>Yang, Mutian</creatorcontrib><creatorcontrib>Cui, Jianwei</creatorcontrib><creatorcontrib>Cui, Linwei</creatorcontrib><creatorcontrib>Huang, Zizheng</creatorcontrib><creatorcontrib>Li, Xiang</creatorcontrib><collection>CrossRef</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>SciTech Premium Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><jtitle>Electronics (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Shang, Yucheng</au><au>Yang, Mutian</au><au>Cui, Jianwei</au><au>Cui, Linwei</au><au>Huang, Zizheng</au><au>Li, Xiang</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Driver Emotion and Fatigue State Detection Based on Time Series Fusion</atitle><jtitle>Electronics (Basel)</jtitle><date>2023-01-01</date><risdate>2023</risdate><volume>12</volume><issue>1</issue><spage>26</spage><pages>26-</pages><issn>2079-9292</issn><eissn>2079-9292</eissn><abstract>Studies have shown that driver fatigue or unpleasant emotions significantly increase driving risks. Detecting driver emotions and fatigue states and providing timely warnings can effectively minimize the incidence of traffic accidents. However, existing models rarely combine driver emotion and fatigue detection, and there is space to improve the accuracy of recognition. In this paper, we propose a non-invasive and efficient detection method for driver fatigue and emotional state, which is the first time to combine them in the detection of driver state. Firstly, the captured video image sequences are preprocessed, and Dlib (image open source processing library) is used to locate face regions and mark key points; secondly, facial features are extracted, and fatigue indicators, such as driver eye closure time (PERCLOS) and yawn frequency are calculated using the dual-threshold method and fused by mathematical methods; thirdly, an improved lightweight RM-Xception convolutional neural network is introduced to identify the driver’s emotional state; finally, the two indicators are fused based on time series to obtain a comprehensive score for evaluating the driver’s state. The results show that the fatigue detection algorithm proposed in this paper has high accuracy, and the accuracy of the emotion recognition network reaches an accuracy rate of 73.32% on the Fer2013 dataset. 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subjects | Accuracy Algorithms Artificial neural networks Automobile drivers Classification Datasets Driver fatigue Electroencephalography Emotion recognition Emotional factors Emotions Fatigue Feature extraction Image processing Indicators Methods Neural networks Object recognition (Computers) Pattern recognition Physiology Psychological aspects Sequences Time series Traffic accidents Traffic models Traffic safety Wavelet transforms |
title | Driver Emotion and Fatigue State Detection Based on Time Series Fusion |
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