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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...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9230121/ https://www.ncbi.nlm.nih.gov/pubmed/35746182 http://dx.doi.org/10.3390/s22124402 |
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author | Oh, Geesung Jeong, Euiseok Kim, Rak Chul Yang, Ji Hyun Hwang, Sungwook Lee, Sangho Lim, Sejoon |
author_facet | Oh, Geesung Jeong, Euiseok Kim, Rak Chul Yang, Ji Hyun Hwang, Sungwook Lee, Sangho Lim, Sejoon |
author_sort | Oh, Geesung |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9230121 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-92301212022-06-25 Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving Oh, Geesung Jeong, Euiseok Kim, Rak Chul Yang, Ji Hyun Hwang, Sungwook Lee, Sangho Lim, Sejoon Sensors (Basel) Article 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. MDPI 2022-06-10 /pmc/articles/PMC9230121/ /pubmed/35746182 http://dx.doi.org/10.3390/s22124402 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/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/). |
spellingShingle | Article Oh, Geesung Jeong, Euiseok Kim, Rak Chul Yang, Ji Hyun Hwang, Sungwook Lee, Sangho Lim, Sejoon Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving |
title | Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving |
title_full | Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving |
title_fullStr | Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving |
title_full_unstemmed | Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving |
title_short | Multimodal Data Collection System for Driver Emotion Recognition Based on Self-Reporting in Real-World Driving |
title_sort | multimodal data collection system for driver emotion recognition based on self-reporting in real-world driving |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9230121/ https://www.ncbi.nlm.nih.gov/pubmed/35746182 http://dx.doi.org/10.3390/s22124402 |
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