Research on tiredness detection method based on fusion of face image and EEG information

Meiyan Zhang; Boqi Zhao; Jiaze Tang; Dan Liu; Qisong Wang; Jinwei Sun; Yongping Zhao

doi:10.1109/ICFTIC54370.2021.9647277

Research on tiredness detection method based on fusion of face image and EEG information

Meiyan Zhang
, Boqi Zhao
, Jiaze Tang
, Dan Liu
, Qisong Wang
, Jinwei Sun
, Yongping Zhao

Harbin Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Drivers in fatigue state tend to have slow response, distraction, and rising operational errors, resulting in reduced reliability of driver-controlled vehicles and increased risk of traffic accidents. It is necessary to detect mental state of drivers, so that we can send out alarm and take corresponding measures timely before traffic accidents. As important physiological indexes of human body, EEG and image have been widely used in medical diagnosis, attention analysis and other fields. This paper mainly studies fatigue feature extraction algorithm and fatigue detection method based on single-mode and multi-modal information. Experiments were also designed to evaluate the proposed fatigue detection methods. The results show that the fatigue detection based on the presented multi-modal information fusion method have greatly improved the accuracy and adaptability.

Original language	English
Title of host publication	2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	396-400
Number of pages	5
ISBN (Electronic)	9781665406055
DOIs	https://doi.org/10.1109/ICFTIC54370.2021.9647277
State	Published - 2021
Externally published	Yes
Event	3rd IEEE International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021 - Virtual, Online, United States Duration: 12 Nov 2021 → 14 Nov 2021

Publication series

Name	2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021

Conference

Conference	3rd IEEE International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021
Country/Territory	United States
City	Virtual, Online
Period	12/11/21 → 14/11/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

Keywords

EEG signals
fatigue driving
image processing
information fusion

Access to Document

10.1109/ICFTIC54370.2021.9647277

Cite this

Zhang, M., Zhao, B., Tang, J., Liu, D., Wang, Q., Sun, J., & Zhao, Y. (2021). Research on tiredness detection method based on fusion of face image and EEG information. In 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021 (pp. 396-400). (2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICFTIC54370.2021.9647277

Zhang, Meiyan ; Zhao, Boqi ; Tang, Jiaze et al. / Research on tiredness detection method based on fusion of face image and EEG information. 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. pp. 396-400 (2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021).

@inproceedings{abd74daeab0849e0b6582ac80b6dc874,

title = "Research on tiredness detection method based on fusion of face image and EEG information",

abstract = "Drivers in fatigue state tend to have slow response, distraction, and rising operational errors, resulting in reduced reliability of driver-controlled vehicles and increased risk of traffic accidents. It is necessary to detect mental state of drivers, so that we can send out alarm and take corresponding measures timely before traffic accidents. As important physiological indexes of human body, EEG and image have been widely used in medical diagnosis, attention analysis and other fields. This paper mainly studies fatigue feature extraction algorithm and fatigue detection method based on single-mode and multi-modal information. Experiments were also designed to evaluate the proposed fatigue detection methods. The results show that the fatigue detection based on the presented multi-modal information fusion method have greatly improved the accuracy and adaptability.",

keywords = "EEG signals, fatigue driving, image processing, information fusion",

author = "Meiyan Zhang and Boqi Zhao and Jiaze Tang and Dan Liu and Qisong Wang and Jinwei Sun and Yongping Zhao",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 3rd IEEE International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021 ; Conference date: 12-11-2021 Through 14-11-2021",

year = "2021",

doi = "10.1109/ICFTIC54370.2021.9647277",

language = "英语",

series = "2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "396--400",

booktitle = "2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021",

address = "美国",

}

Zhang, M, Zhao, B, Tang, J, Liu, D, Wang, Q , Sun, J & Zhao, Y 2021, Research on tiredness detection method based on fusion of face image and EEG information. in 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021. 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021, Institute of Electrical and Electronics Engineers Inc., pp. 396-400, 3rd IEEE International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021, Virtual, Online, United States, 12/11/21. https://doi.org/10.1109/ICFTIC54370.2021.9647277

Research on tiredness detection method based on fusion of face image and EEG information. / Zhang, Meiyan; Zhao, Boqi; Tang, Jiaze et al.
2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. p. 396-400 (2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Research on tiredness detection method based on fusion of face image and EEG information

AU - Zhang, Meiyan

AU - Zhao, Boqi

AU - Tang, Jiaze

AU - Liu, Dan

AU - Wang, Qisong

AU - Sun, Jinwei

AU - Zhao, Yongping

PY - 2021

Y1 - 2021

N2 - Drivers in fatigue state tend to have slow response, distraction, and rising operational errors, resulting in reduced reliability of driver-controlled vehicles and increased risk of traffic accidents. It is necessary to detect mental state of drivers, so that we can send out alarm and take corresponding measures timely before traffic accidents. As important physiological indexes of human body, EEG and image have been widely used in medical diagnosis, attention analysis and other fields. This paper mainly studies fatigue feature extraction algorithm and fatigue detection method based on single-mode and multi-modal information. Experiments were also designed to evaluate the proposed fatigue detection methods. The results show that the fatigue detection based on the presented multi-modal information fusion method have greatly improved the accuracy and adaptability.

AB - Drivers in fatigue state tend to have slow response, distraction, and rising operational errors, resulting in reduced reliability of driver-controlled vehicles and increased risk of traffic accidents. It is necessary to detect mental state of drivers, so that we can send out alarm and take corresponding measures timely before traffic accidents. As important physiological indexes of human body, EEG and image have been widely used in medical diagnosis, attention analysis and other fields. This paper mainly studies fatigue feature extraction algorithm and fatigue detection method based on single-mode and multi-modal information. Experiments were also designed to evaluate the proposed fatigue detection methods. The results show that the fatigue detection based on the presented multi-modal information fusion method have greatly improved the accuracy and adaptability.

KW - EEG signals

KW - fatigue driving

KW - image processing

KW - information fusion

UR - https://www.scopus.com/pages/publications/85124358003

U2 - 10.1109/ICFTIC54370.2021.9647277

DO - 10.1109/ICFTIC54370.2021.9647277

M3 - 会议稿件

AN - SCOPUS:85124358003

T3 - 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021

SP - 396

EP - 400

BT - 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 3rd IEEE International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021

Y2 - 12 November 2021 through 14 November 2021

ER -

Zhang M, Zhao B, Tang J, Liu D, Wang Q , Sun J et al. Research on tiredness detection method based on fusion of face image and EEG information. In 2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021. Institute of Electrical and Electronics Engineers Inc. 2021. p. 396-400. (2021 IEEE 3rd International Conference on Frontiers Technology of Information and Computer, ICFTIC 2021). doi: 10.1109/ICFTIC54370.2021.9647277

Research on tiredness detection method based on fusion of face image and EEG information

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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