Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy

Experiencing frustration while driving can harm cognitive processing, result in aggressive behavior and hence negatively influence driving performance and traffic safety. Being able to automatically detect frustration would allow adaptive driver assistance and automation systems to adequately react to a driver’s frustration and mitigate potential negative consequences. To identify reliable and valid indicators of driver’s frustration, we conducted two driving simulator experiments. In the first experiment, we aimed to reveal facial expressions that indicate frustration in continuous video recordings of the driver’s face taken while driving highly realistic simulator scenarios in which frustrated or non-frustrated emotional states were experienced. An automated analysis of facial expressions combined with multivariate logistic regression classification revealed that frustrated time intervals can be discriminated from non-frustrated ones with accuracy of 62.0% (mean over 30 participants). A further analysis of the facial expressions revealed that frustrated drivers tend to activate muscles in the mouth region (chin raiser, lip pucker, lip pressor). In the second experiment, we measured cortical activation with almost whole-head functional near-infrared spectroscopy (fNIRS) while participants experienced frustrating and non-frustrating driving simulator scenarios. Multivariate logistic regression applied to the fNIRS measurements allowed us to discriminate between frustrated and non-frustrated driving intervals with higher accuracy of 78.1% (mean over 12 participants). Frustrated driving intervals were indicated by increased activation in the inferior frontal, putative premotor and occipito-temporal cortices. Our results show that facial and cortical markers of frustration can be informative for time resolved driver state identification in complex realistic driving situations. The markers derived here can potentially be used as an input for future adaptive driver assistance and automation systems that detect driver frustration and adaptively react to mitigate it. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Klas Ihme [verfasserIn] Anirudh Unni [verfasserIn] Meng Zhang [verfasserIn] Jochem W. Rieger [verfasserIn] Meike Jipp [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2018

Schlagwörter:	frustration driver state recognition facial expressions functional near-infrared spectroscopy adaptive automation

Übergeordnetes Werk:	In: Frontiers in Human Neuroscience - Frontiers Media S.A., 2008, 12(2018)
Übergeordnetes Werk:	volume:12 ; year:2018

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3389/fnhum.2018.00327

Katalog-ID:	DOAJ071216715

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allfields	10.3389/fnhum.2018.00327 doi (DE-627)DOAJ071216715 (DE-599)DOAJbe51fa774d73425a9093a3301a6dba7a DE-627 ger DE-627 rakwb eng RC321-571 Klas Ihme verfasserin aut Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Experiencing frustration while driving can harm cognitive processing, result in aggressive behavior and hence negatively influence driving performance and traffic safety. Being able to automatically detect frustration would allow adaptive driver assistance and automation systems to adequately react to a driver’s frustration and mitigate potential negative consequences. To identify reliable and valid indicators of driver’s frustration, we conducted two driving simulator experiments. In the first experiment, we aimed to reveal facial expressions that indicate frustration in continuous video recordings of the driver’s face taken while driving highly realistic simulator scenarios in which frustrated or non-frustrated emotional states were experienced. An automated analysis of facial expressions combined with multivariate logistic regression classification revealed that frustrated time intervals can be discriminated from non-frustrated ones with accuracy of 62.0% (mean over 30 participants). A further analysis of the facial expressions revealed that frustrated drivers tend to activate muscles in the mouth region (chin raiser, lip pucker, lip pressor). In the second experiment, we measured cortical activation with almost whole-head functional near-infrared spectroscopy (fNIRS) while participants experienced frustrating and non-frustrating driving simulator scenarios. Multivariate logistic regression applied to the fNIRS measurements allowed us to discriminate between frustrated and non-frustrated driving intervals with higher accuracy of 78.1% (mean over 12 participants). Frustrated driving intervals were indicated by increased activation in the inferior frontal, putative premotor and occipito-temporal cortices. Our results show that facial and cortical markers of frustration can be informative for time resolved driver state identification in complex realistic driving situations. The markers derived here can potentially be used as an input for future adaptive driver assistance and automation systems that detect driver frustration and adaptively react to mitigate it. frustration driver state recognition facial expressions functional near-infrared spectroscopy adaptive automation Neurosciences. Biological psychiatry. Neuropsychiatry Anirudh Unni verfasserin aut Meng Zhang verfasserin aut Jochem W. Rieger verfasserin aut Meike Jipp verfasserin aut In Frontiers in Human Neuroscience Frontiers Media S.A., 2008 12(2018) (DE-627)56601243X (DE-600)2425477-0 16625161 nnns volume:12 year:2018 https://doi.org/10.3389/fnhum.2018.00327 kostenfrei https://doaj.org/article/be51fa774d73425a9093a3301a6dba7a kostenfrei https://www.frontiersin.org/article/10.3389/fnhum.2018.00327/full kostenfrei https://doaj.org/toc/1662-5161 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2018
spelling	10.3389/fnhum.2018.00327 doi (DE-627)DOAJ071216715 (DE-599)DOAJbe51fa774d73425a9093a3301a6dba7a DE-627 ger DE-627 rakwb eng RC321-571 Klas Ihme verfasserin aut Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Experiencing frustration while driving can harm cognitive processing, result in aggressive behavior and hence negatively influence driving performance and traffic safety. Being able to automatically detect frustration would allow adaptive driver assistance and automation systems to adequately react to a driver’s frustration and mitigate potential negative consequences. To identify reliable and valid indicators of driver’s frustration, we conducted two driving simulator experiments. In the first experiment, we aimed to reveal facial expressions that indicate frustration in continuous video recordings of the driver’s face taken while driving highly realistic simulator scenarios in which frustrated or non-frustrated emotional states were experienced. An automated analysis of facial expressions combined with multivariate logistic regression classification revealed that frustrated time intervals can be discriminated from non-frustrated ones with accuracy of 62.0% (mean over 30 participants). A further analysis of the facial expressions revealed that frustrated drivers tend to activate muscles in the mouth region (chin raiser, lip pucker, lip pressor). In the second experiment, we measured cortical activation with almost whole-head functional near-infrared spectroscopy (fNIRS) while participants experienced frustrating and non-frustrating driving simulator scenarios. Multivariate logistic regression applied to the fNIRS measurements allowed us to discriminate between frustrated and non-frustrated driving intervals with higher accuracy of 78.1% (mean over 12 participants). Frustrated driving intervals were indicated by increased activation in the inferior frontal, putative premotor and occipito-temporal cortices. Our results show that facial and cortical markers of frustration can be informative for time resolved driver state identification in complex realistic driving situations. The markers derived here can potentially be used as an input for future adaptive driver assistance and automation systems that detect driver frustration and adaptively react to mitigate it. frustration driver state recognition facial expressions functional near-infrared spectroscopy adaptive automation Neurosciences. Biological psychiatry. Neuropsychiatry Anirudh Unni verfasserin aut Meng Zhang verfasserin aut Jochem W. Rieger verfasserin aut Meike Jipp verfasserin aut In Frontiers in Human Neuroscience Frontiers Media S.A., 2008 12(2018) (DE-627)56601243X (DE-600)2425477-0 16625161 nnns volume:12 year:2018 https://doi.org/10.3389/fnhum.2018.00327 kostenfrei https://doaj.org/article/be51fa774d73425a9093a3301a6dba7a kostenfrei https://www.frontiersin.org/article/10.3389/fnhum.2018.00327/full kostenfrei https://doaj.org/toc/1662-5161 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2018
allfields_unstemmed	10.3389/fnhum.2018.00327 doi (DE-627)DOAJ071216715 (DE-599)DOAJbe51fa774d73425a9093a3301a6dba7a DE-627 ger DE-627 rakwb eng RC321-571 Klas Ihme verfasserin aut Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Experiencing frustration while driving can harm cognitive processing, result in aggressive behavior and hence negatively influence driving performance and traffic safety. Being able to automatically detect frustration would allow adaptive driver assistance and automation systems to adequately react to a driver’s frustration and mitigate potential negative consequences. To identify reliable and valid indicators of driver’s frustration, we conducted two driving simulator experiments. In the first experiment, we aimed to reveal facial expressions that indicate frustration in continuous video recordings of the driver’s face taken while driving highly realistic simulator scenarios in which frustrated or non-frustrated emotional states were experienced. An automated analysis of facial expressions combined with multivariate logistic regression classification revealed that frustrated time intervals can be discriminated from non-frustrated ones with accuracy of 62.0% (mean over 30 participants). A further analysis of the facial expressions revealed that frustrated drivers tend to activate muscles in the mouth region (chin raiser, lip pucker, lip pressor). In the second experiment, we measured cortical activation with almost whole-head functional near-infrared spectroscopy (fNIRS) while participants experienced frustrating and non-frustrating driving simulator scenarios. Multivariate logistic regression applied to the fNIRS measurements allowed us to discriminate between frustrated and non-frustrated driving intervals with higher accuracy of 78.1% (mean over 12 participants). Frustrated driving intervals were indicated by increased activation in the inferior frontal, putative premotor and occipito-temporal cortices. Our results show that facial and cortical markers of frustration can be informative for time resolved driver state identification in complex realistic driving situations. The markers derived here can potentially be used as an input for future adaptive driver assistance and automation systems that detect driver frustration and adaptively react to mitigate it. frustration driver state recognition facial expressions functional near-infrared spectroscopy adaptive automation Neurosciences. Biological psychiatry. Neuropsychiatry Anirudh Unni verfasserin aut Meng Zhang verfasserin aut Jochem W. Rieger verfasserin aut Meike Jipp verfasserin aut In Frontiers in Human Neuroscience Frontiers Media S.A., 2008 12(2018) (DE-627)56601243X (DE-600)2425477-0 16625161 nnns volume:12 year:2018 https://doi.org/10.3389/fnhum.2018.00327 kostenfrei https://doaj.org/article/be51fa774d73425a9093a3301a6dba7a kostenfrei https://www.frontiersin.org/article/10.3389/fnhum.2018.00327/full kostenfrei https://doaj.org/toc/1662-5161 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2018
allfieldsGer	10.3389/fnhum.2018.00327 doi (DE-627)DOAJ071216715 (DE-599)DOAJbe51fa774d73425a9093a3301a6dba7a DE-627 ger DE-627 rakwb eng RC321-571 Klas Ihme verfasserin aut Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Experiencing frustration while driving can harm cognitive processing, result in aggressive behavior and hence negatively influence driving performance and traffic safety. Being able to automatically detect frustration would allow adaptive driver assistance and automation systems to adequately react to a driver’s frustration and mitigate potential negative consequences. To identify reliable and valid indicators of driver’s frustration, we conducted two driving simulator experiments. In the first experiment, we aimed to reveal facial expressions that indicate frustration in continuous video recordings of the driver’s face taken while driving highly realistic simulator scenarios in which frustrated or non-frustrated emotional states were experienced. An automated analysis of facial expressions combined with multivariate logistic regression classification revealed that frustrated time intervals can be discriminated from non-frustrated ones with accuracy of 62.0% (mean over 30 participants). A further analysis of the facial expressions revealed that frustrated drivers tend to activate muscles in the mouth region (chin raiser, lip pucker, lip pressor). In the second experiment, we measured cortical activation with almost whole-head functional near-infrared spectroscopy (fNIRS) while participants experienced frustrating and non-frustrating driving simulator scenarios. Multivariate logistic regression applied to the fNIRS measurements allowed us to discriminate between frustrated and non-frustrated driving intervals with higher accuracy of 78.1% (mean over 12 participants). Frustrated driving intervals were indicated by increased activation in the inferior frontal, putative premotor and occipito-temporal cortices. Our results show that facial and cortical markers of frustration can be informative for time resolved driver state identification in complex realistic driving situations. The markers derived here can potentially be used as an input for future adaptive driver assistance and automation systems that detect driver frustration and adaptively react to mitigate it. frustration driver state recognition facial expressions functional near-infrared spectroscopy adaptive automation Neurosciences. Biological psychiatry. Neuropsychiatry Anirudh Unni verfasserin aut Meng Zhang verfasserin aut Jochem W. Rieger verfasserin aut Meike Jipp verfasserin aut In Frontiers in Human Neuroscience Frontiers Media S.A., 2008 12(2018) (DE-627)56601243X (DE-600)2425477-0 16625161 nnns volume:12 year:2018 https://doi.org/10.3389/fnhum.2018.00327 kostenfrei https://doaj.org/article/be51fa774d73425a9093a3301a6dba7a kostenfrei https://www.frontiersin.org/article/10.3389/fnhum.2018.00327/full kostenfrei https://doaj.org/toc/1662-5161 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2018
allfieldsSound	10.3389/fnhum.2018.00327 doi (DE-627)DOAJ071216715 (DE-599)DOAJbe51fa774d73425a9093a3301a6dba7a DE-627 ger DE-627 rakwb eng RC321-571 Klas Ihme verfasserin aut Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy 2018 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Experiencing frustration while driving can harm cognitive processing, result in aggressive behavior and hence negatively influence driving performance and traffic safety. Being able to automatically detect frustration would allow adaptive driver assistance and automation systems to adequately react to a driver’s frustration and mitigate potential negative consequences. To identify reliable and valid indicators of driver’s frustration, we conducted two driving simulator experiments. In the first experiment, we aimed to reveal facial expressions that indicate frustration in continuous video recordings of the driver’s face taken while driving highly realistic simulator scenarios in which frustrated or non-frustrated emotional states were experienced. An automated analysis of facial expressions combined with multivariate logistic regression classification revealed that frustrated time intervals can be discriminated from non-frustrated ones with accuracy of 62.0% (mean over 30 participants). A further analysis of the facial expressions revealed that frustrated drivers tend to activate muscles in the mouth region (chin raiser, lip pucker, lip pressor). In the second experiment, we measured cortical activation with almost whole-head functional near-infrared spectroscopy (fNIRS) while participants experienced frustrating and non-frustrating driving simulator scenarios. Multivariate logistic regression applied to the fNIRS measurements allowed us to discriminate between frustrated and non-frustrated driving intervals with higher accuracy of 78.1% (mean over 12 participants). Frustrated driving intervals were indicated by increased activation in the inferior frontal, putative premotor and occipito-temporal cortices. Our results show that facial and cortical markers of frustration can be informative for time resolved driver state identification in complex realistic driving situations. The markers derived here can potentially be used as an input for future adaptive driver assistance and automation systems that detect driver frustration and adaptively react to mitigate it. frustration driver state recognition facial expressions functional near-infrared spectroscopy adaptive automation Neurosciences. Biological psychiatry. Neuropsychiatry Anirudh Unni verfasserin aut Meng Zhang verfasserin aut Jochem W. Rieger verfasserin aut Meike Jipp verfasserin aut In Frontiers in Human Neuroscience Frontiers Media S.A., 2008 12(2018) (DE-627)56601243X (DE-600)2425477-0 16625161 nnns volume:12 year:2018 https://doi.org/10.3389/fnhum.2018.00327 kostenfrei https://doaj.org/article/be51fa774d73425a9093a3301a6dba7a kostenfrei https://www.frontiersin.org/article/10.3389/fnhum.2018.00327/full kostenfrei https://doaj.org/toc/1662-5161 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 12 2018
language	English
source	In Frontiers in Human Neuroscience 12(2018) volume:12 year:2018
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topic_facet	frustration driver state recognition facial expressions functional near-infrared spectroscopy adaptive automation Neurosciences. Biological psychiatry. Neuropsychiatry
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authorswithroles_txt_mv	Klas Ihme @@aut@@ Anirudh Unni @@aut@@ Meng Zhang @@aut@@ Jochem W. Rieger @@aut@@ Meike Jipp @@aut@@
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callnumber-first	R - Medicine
author	Klas Ihme
spellingShingle	Klas Ihme misc RC321-571 misc frustration misc driver state recognition misc facial expressions misc functional near-infrared spectroscopy misc adaptive automation misc Neurosciences. Biological psychiatry. Neuropsychiatry Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy
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topic_title	RC321-571 Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy frustration driver state recognition facial expressions functional near-infrared spectroscopy adaptive automation
topic	misc RC321-571 misc frustration misc driver state recognition misc facial expressions misc functional near-infrared spectroscopy misc adaptive automation misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_unstemmed	misc RC321-571 misc frustration misc driver state recognition misc facial expressions misc functional near-infrared spectroscopy misc adaptive automation misc Neurosciences. Biological psychiatry. Neuropsychiatry
topic_browse	misc RC321-571 misc frustration misc driver state recognition misc facial expressions misc functional near-infrared spectroscopy misc adaptive automation misc Neurosciences. Biological psychiatry. Neuropsychiatry
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title	Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy
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title_full	Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy
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author_browse	Klas Ihme Anirudh Unni Meng Zhang Jochem W. Rieger Meike Jipp
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title_sort	recognizing frustration of drivers from face video recordings and brain activation measurements with functional near-infrared spectroscopy
callnumber	RC321-571
title_auth	Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy
abstract	Experiencing frustration while driving can harm cognitive processing, result in aggressive behavior and hence negatively influence driving performance and traffic safety. Being able to automatically detect frustration would allow adaptive driver assistance and automation systems to adequately react to a driver’s frustration and mitigate potential negative consequences. To identify reliable and valid indicators of driver’s frustration, we conducted two driving simulator experiments. In the first experiment, we aimed to reveal facial expressions that indicate frustration in continuous video recordings of the driver’s face taken while driving highly realistic simulator scenarios in which frustrated or non-frustrated emotional states were experienced. An automated analysis of facial expressions combined with multivariate logistic regression classification revealed that frustrated time intervals can be discriminated from non-frustrated ones with accuracy of 62.0% (mean over 30 participants). A further analysis of the facial expressions revealed that frustrated drivers tend to activate muscles in the mouth region (chin raiser, lip pucker, lip pressor). In the second experiment, we measured cortical activation with almost whole-head functional near-infrared spectroscopy (fNIRS) while participants experienced frustrating and non-frustrating driving simulator scenarios. Multivariate logistic regression applied to the fNIRS measurements allowed us to discriminate between frustrated and non-frustrated driving intervals with higher accuracy of 78.1% (mean over 12 participants). Frustrated driving intervals were indicated by increased activation in the inferior frontal, putative premotor and occipito-temporal cortices. Our results show that facial and cortical markers of frustration can be informative for time resolved driver state identification in complex realistic driving situations. The markers derived here can potentially be used as an input for future adaptive driver assistance and automation systems that detect driver frustration and adaptively react to mitigate it.
abstractGer	Experiencing frustration while driving can harm cognitive processing, result in aggressive behavior and hence negatively influence driving performance and traffic safety. Being able to automatically detect frustration would allow adaptive driver assistance and automation systems to adequately react to a driver’s frustration and mitigate potential negative consequences. To identify reliable and valid indicators of driver’s frustration, we conducted two driving simulator experiments. In the first experiment, we aimed to reveal facial expressions that indicate frustration in continuous video recordings of the driver’s face taken while driving highly realistic simulator scenarios in which frustrated or non-frustrated emotional states were experienced. An automated analysis of facial expressions combined with multivariate logistic regression classification revealed that frustrated time intervals can be discriminated from non-frustrated ones with accuracy of 62.0% (mean over 30 participants). A further analysis of the facial expressions revealed that frustrated drivers tend to activate muscles in the mouth region (chin raiser, lip pucker, lip pressor). In the second experiment, we measured cortical activation with almost whole-head functional near-infrared spectroscopy (fNIRS) while participants experienced frustrating and non-frustrating driving simulator scenarios. Multivariate logistic regression applied to the fNIRS measurements allowed us to discriminate between frustrated and non-frustrated driving intervals with higher accuracy of 78.1% (mean over 12 participants). Frustrated driving intervals were indicated by increased activation in the inferior frontal, putative premotor and occipito-temporal cortices. Our results show that facial and cortical markers of frustration can be informative for time resolved driver state identification in complex realistic driving situations. The markers derived here can potentially be used as an input for future adaptive driver assistance and automation systems that detect driver frustration and adaptively react to mitigate it.
abstract_unstemmed	Experiencing frustration while driving can harm cognitive processing, result in aggressive behavior and hence negatively influence driving performance and traffic safety. Being able to automatically detect frustration would allow adaptive driver assistance and automation systems to adequately react to a driver’s frustration and mitigate potential negative consequences. To identify reliable and valid indicators of driver’s frustration, we conducted two driving simulator experiments. In the first experiment, we aimed to reveal facial expressions that indicate frustration in continuous video recordings of the driver’s face taken while driving highly realistic simulator scenarios in which frustrated or non-frustrated emotional states were experienced. An automated analysis of facial expressions combined with multivariate logistic regression classification revealed that frustrated time intervals can be discriminated from non-frustrated ones with accuracy of 62.0% (mean over 30 participants). A further analysis of the facial expressions revealed that frustrated drivers tend to activate muscles in the mouth region (chin raiser, lip pucker, lip pressor). In the second experiment, we measured cortical activation with almost whole-head functional near-infrared spectroscopy (fNIRS) while participants experienced frustrating and non-frustrating driving simulator scenarios. Multivariate logistic regression applied to the fNIRS measurements allowed us to discriminate between frustrated and non-frustrated driving intervals with higher accuracy of 78.1% (mean over 12 participants). Frustrated driving intervals were indicated by increased activation in the inferior frontal, putative premotor and occipito-temporal cortices. Our results show that facial and cortical markers of frustration can be informative for time resolved driver state identification in complex realistic driving situations. The markers derived here can potentially be used as an input for future adaptive driver assistance and automation systems that detect driver frustration and adaptively react to mitigate it.
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title_short	Recognizing Frustration of Drivers From Face Video Recordings and Brain Activation Measurements With Functional Near-Infrared Spectroscopy
url	https://doi.org/10.3389/fnhum.2018.00327 https://doaj.org/article/be51fa774d73425a9093a3301a6dba7a https://www.frontiersin.org/article/10.3389/fnhum.2018.00327/full https://doaj.org/toc/1662-5161
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