Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving

As vehicles provide various services to drivers, research on driver emotion recognition has been expanding. However, current driver emotion datasets are limited by inconsistencies in collected data and inferred emotional state annotations by others. To overcome this limitation, we propose a data col...
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Gespeichert in:

Autor*in:	Geesung Oh [verfasserIn] Euiseok Jeong [verfasserIn] Rak Chul Kim [verfasserIn] Ji Hyun Yang [verfasserIn] Sungwook Hwang [verfasserIn] Sangho Lee [verfasserIn] Sejoon Lim [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	driver emotion recognition multimodal self-report real-world driving

Übergeordnetes Werk:	In: Sensors - MDPI AG, 2003, 22(2022), 12, p 4402
Übergeordnetes Werk:	volume:22 ; year:2022 ; number:12, p 4402

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.3390/s22124402

Katalog-ID:	DOAJ025562762

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520			\|a As vehicles provide various services to drivers, research on driver emotion recognition has been expanding. However, current driver emotion datasets are limited by inconsistencies in collected data and inferred emotional state annotations by others. To overcome this limitation, we propose a data collection system that collects multimodal datasets during real-world driving. The proposed system includes a self-reportable HMI application into which a driver directly inputs their current emotion state. Data collection was completed without any accidents for over 122 h of real-world driving using the system, which also considers the minimization of behavioral and cognitive disturbances. To demonstrate the validity of our collected dataset, we also provide case studies for statistical analysis, driver face detection, and personalized driver emotion recognition. The proposed data collection system enables the construction of reliable large-scale datasets on real-world driving and facilitates research on driver emotion recognition. The proposed system is avaliable on GitHub.
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allfields	10.3390/s22124402 doi (DE-627)DOAJ025562762 (DE-599)DOAJ38623b75972c43d59c86e742d01d619b DE-627 ger DE-627 rakwb eng TP1-1185 Geesung Oh verfasserin aut Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As vehicles provide various services to drivers, research on driver emotion recognition has been expanding. However, current driver emotion datasets are limited by inconsistencies in collected data and inferred emotional state annotations by others. To overcome this limitation, we propose a data collection system that collects multimodal datasets during real-world driving. The proposed system includes a self-reportable HMI application into which a driver directly inputs their current emotion state. Data collection was completed without any accidents for over 122 h of real-world driving using the system, which also considers the minimization of behavioral and cognitive disturbances. To demonstrate the validity of our collected dataset, we also provide case studies for statistical analysis, driver face detection, and personalized driver emotion recognition. The proposed data collection system enables the construction of reliable large-scale datasets on real-world driving and facilitates research on driver emotion recognition. The proposed system is avaliable on GitHub. driver emotion recognition multimodal self-report real-world driving Chemical technology Euiseok Jeong verfasserin aut Rak Chul Kim verfasserin aut Ji Hyun Yang verfasserin aut Sungwook Hwang verfasserin aut Sangho Lee verfasserin aut Sejoon Lim verfasserin aut In Sensors MDPI AG, 2003 22(2022), 12, p 4402 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:12, p 4402 https://doi.org/10.3390/s22124402 kostenfrei https://doaj.org/article/38623b75972c43d59c86e742d01d619b kostenfrei https://www.mdpi.com/1424-8220/22/12/4402 kostenfrei https://doaj.org/toc/1424-8220 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_70 GBV_ILN_73 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_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 12, p 4402
spelling	10.3390/s22124402 doi (DE-627)DOAJ025562762 (DE-599)DOAJ38623b75972c43d59c86e742d01d619b DE-627 ger DE-627 rakwb eng TP1-1185 Geesung Oh verfasserin aut Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As vehicles provide various services to drivers, research on driver emotion recognition has been expanding. However, current driver emotion datasets are limited by inconsistencies in collected data and inferred emotional state annotations by others. To overcome this limitation, we propose a data collection system that collects multimodal datasets during real-world driving. The proposed system includes a self-reportable HMI application into which a driver directly inputs their current emotion state. Data collection was completed without any accidents for over 122 h of real-world driving using the system, which also considers the minimization of behavioral and cognitive disturbances. To demonstrate the validity of our collected dataset, we also provide case studies for statistical analysis, driver face detection, and personalized driver emotion recognition. The proposed data collection system enables the construction of reliable large-scale datasets on real-world driving and facilitates research on driver emotion recognition. The proposed system is avaliable on GitHub. driver emotion recognition multimodal self-report real-world driving Chemical technology Euiseok Jeong verfasserin aut Rak Chul Kim verfasserin aut Ji Hyun Yang verfasserin aut Sungwook Hwang verfasserin aut Sangho Lee verfasserin aut Sejoon Lim verfasserin aut In Sensors MDPI AG, 2003 22(2022), 12, p 4402 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:12, p 4402 https://doi.org/10.3390/s22124402 kostenfrei https://doaj.org/article/38623b75972c43d59c86e742d01d619b kostenfrei https://www.mdpi.com/1424-8220/22/12/4402 kostenfrei https://doaj.org/toc/1424-8220 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_70 GBV_ILN_73 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_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 12, p 4402
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allfieldsGer	10.3390/s22124402 doi (DE-627)DOAJ025562762 (DE-599)DOAJ38623b75972c43d59c86e742d01d619b DE-627 ger DE-627 rakwb eng TP1-1185 Geesung Oh verfasserin aut Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As vehicles provide various services to drivers, research on driver emotion recognition has been expanding. However, current driver emotion datasets are limited by inconsistencies in collected data and inferred emotional state annotations by others. To overcome this limitation, we propose a data collection system that collects multimodal datasets during real-world driving. The proposed system includes a self-reportable HMI application into which a driver directly inputs their current emotion state. Data collection was completed without any accidents for over 122 h of real-world driving using the system, which also considers the minimization of behavioral and cognitive disturbances. To demonstrate the validity of our collected dataset, we also provide case studies for statistical analysis, driver face detection, and personalized driver emotion recognition. The proposed data collection system enables the construction of reliable large-scale datasets on real-world driving and facilitates research on driver emotion recognition. The proposed system is avaliable on GitHub. driver emotion recognition multimodal self-report real-world driving Chemical technology Euiseok Jeong verfasserin aut Rak Chul Kim verfasserin aut Ji Hyun Yang verfasserin aut Sungwook Hwang verfasserin aut Sangho Lee verfasserin aut Sejoon Lim verfasserin aut In Sensors MDPI AG, 2003 22(2022), 12, p 4402 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:12, p 4402 https://doi.org/10.3390/s22124402 kostenfrei https://doaj.org/article/38623b75972c43d59c86e742d01d619b kostenfrei https://www.mdpi.com/1424-8220/22/12/4402 kostenfrei https://doaj.org/toc/1424-8220 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_70 GBV_ILN_73 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_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 12, p 4402
allfieldsSound	10.3390/s22124402 doi (DE-627)DOAJ025562762 (DE-599)DOAJ38623b75972c43d59c86e742d01d619b DE-627 ger DE-627 rakwb eng TP1-1185 Geesung Oh verfasserin aut Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier As vehicles provide various services to drivers, research on driver emotion recognition has been expanding. However, current driver emotion datasets are limited by inconsistencies in collected data and inferred emotional state annotations by others. To overcome this limitation, we propose a data collection system that collects multimodal datasets during real-world driving. The proposed system includes a self-reportable HMI application into which a driver directly inputs their current emotion state. Data collection was completed without any accidents for over 122 h of real-world driving using the system, which also considers the minimization of behavioral and cognitive disturbances. To demonstrate the validity of our collected dataset, we also provide case studies for statistical analysis, driver face detection, and personalized driver emotion recognition. The proposed data collection system enables the construction of reliable large-scale datasets on real-world driving and facilitates research on driver emotion recognition. The proposed system is avaliable on GitHub. driver emotion recognition multimodal self-report real-world driving Chemical technology Euiseok Jeong verfasserin aut Rak Chul Kim verfasserin aut Ji Hyun Yang verfasserin aut Sungwook Hwang verfasserin aut Sangho Lee verfasserin aut Sejoon Lim verfasserin aut In Sensors MDPI AG, 2003 22(2022), 12, p 4402 (DE-627)331640910 (DE-600)2052857-7 14248220 nnns volume:22 year:2022 number:12, p 4402 https://doi.org/10.3390/s22124402 kostenfrei https://doaj.org/article/38623b75972c43d59c86e742d01d619b kostenfrei https://www.mdpi.com/1424-8220/22/12/4402 kostenfrei https://doaj.org/toc/1424-8220 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_70 GBV_ILN_73 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_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2111 GBV_ILN_2507 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_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 22 2022 12, p 4402
language	English
source	In Sensors 22(2022), 12, p 4402 volume:22 year:2022 number:12, p 4402
sourceStr	In Sensors 22(2022), 12, p 4402 volume:22 year:2022 number:12, p 4402
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	driver emotion recognition multimodal self-report real-world driving Chemical technology
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container_title	Sensors
authorswithroles_txt_mv	Geesung Oh @@aut@@ Euiseok Jeong @@aut@@ Rak Chul Kim @@aut@@ Ji Hyun Yang @@aut@@ Sungwook Hwang @@aut@@ Sangho Lee @@aut@@ Sejoon Lim @@aut@@
publishDateDaySort_date	2022-01-01T00:00:00Z
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callnumber-first	T - Technology
author	Geesung Oh
spellingShingle	Geesung Oh misc TP1-1185 misc driver emotion recognition misc multimodal misc self-report misc real-world driving misc Chemical technology Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving
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topic_title	TP1-1185 Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving driver emotion recognition multimodal self-report real-world driving
topic	misc TP1-1185 misc driver emotion recognition misc multimodal misc self-report misc real-world driving misc Chemical technology
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title	Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving
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title_sort	multimodal data collection system for driver emotion recognition based on self-reporting in real-world driving
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title_auth	Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving
abstract	As vehicles provide various services to drivers, research on driver emotion recognition has been expanding. However, current driver emotion datasets are limited by inconsistencies in collected data and inferred emotional state annotations by others. To overcome this limitation, we propose a data collection system that collects multimodal datasets during real-world driving. The proposed system includes a self-reportable HMI application into which a driver directly inputs their current emotion state. Data collection was completed without any accidents for over 122 h of real-world driving using the system, which also considers the minimization of behavioral and cognitive disturbances. To demonstrate the validity of our collected dataset, we also provide case studies for statistical analysis, driver face detection, and personalized driver emotion recognition. The proposed data collection system enables the construction of reliable large-scale datasets on real-world driving and facilitates research on driver emotion recognition. The proposed system is avaliable on GitHub.
abstractGer	As vehicles provide various services to drivers, research on driver emotion recognition has been expanding. However, current driver emotion datasets are limited by inconsistencies in collected data and inferred emotional state annotations by others. To overcome this limitation, we propose a data collection system that collects multimodal datasets during real-world driving. The proposed system includes a self-reportable HMI application into which a driver directly inputs their current emotion state. Data collection was completed without any accidents for over 122 h of real-world driving using the system, which also considers the minimization of behavioral and cognitive disturbances. To demonstrate the validity of our collected dataset, we also provide case studies for statistical analysis, driver face detection, and personalized driver emotion recognition. The proposed data collection system enables the construction of reliable large-scale datasets on real-world driving and facilitates research on driver emotion recognition. The proposed system is avaliable on GitHub.
abstract_unstemmed	As vehicles provide various services to drivers, research on driver emotion recognition has been expanding. However, current driver emotion datasets are limited by inconsistencies in collected data and inferred emotional state annotations by others. To overcome this limitation, we propose a data collection system that collects multimodal datasets during real-world driving. The proposed system includes a self-reportable HMI application into which a driver directly inputs their current emotion state. Data collection was completed without any accidents for over 122 h of real-world driving using the system, which also considers the minimization of behavioral and cognitive disturbances. To demonstrate the validity of our collected dataset, we also provide case studies for statistical analysis, driver face detection, and personalized driver emotion recognition. The proposed data collection system enables the construction of reliable large-scale datasets on real-world driving and facilitates research on driver emotion recognition. The proposed system is avaliable on GitHub.
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title_short	Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving
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