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A Study of Driver Fatigue States in Multiple Scenarios Based on the Fatigue and Sleepiness Indicator

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HCI International 2022 - Late Breaking Papers. Design, User Experience and Interaction (HCII 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13516))

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Abstract

In order to solve the problem that the fatigue indicators of both EEG and ECG are not in the same evaluation dimension in the study of driver fatigue state under different emotions, this paper introduces the conceptual formula of the Fatigue and Sleepiness Indicator for conducting the processing of fatigue indicators in the late data, and normalizes the data of several types of different evaluation dimensions to get the data in the same evaluation dimension, which facilitates the integration of EEG and ECG data. In this paper, (α + θ)/β was used as the EEG fatigue index, and LF/HF was used as the ECG fatigue index; the EEG fatigue index and ECG fatigue index were processed using the conceptual formula of the Fatigue and Sleepiness Indicator to obtain the EEG and ECG exertional fatigue values. Finally, the data were fused using principal component analysis to obtain the integrated fatigue index, i.e., the Fatigue and Sleepiness Indicator. The comparison of the values of the degree of fatigue showed that in the two kinds of brain load driving tasks, the emergence of emotions that distinguish the envisaged will increase the driver’s brain load, thus increasing the degree of fatigue of the driver, the performance of the degree of fatigue of the driver in the usual scenario without emotional fluctuation state is between the pleasant emotion and sad emotion, the fatigue of the driver in the pleasant emotion is the lowest, and the effect of the happy group on the degree of fatigue in the fatigue driving stage is significantly higher than the effect of the sad group on the degree of fatigue. By this study, differences in driver fatigue performance under different emotions were obtained, and the applicability of the conceptual formula of fatigue degree in the fusion of EEG and ECG data was tested.

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Authors and Affiliations

  1. School of Mechanics and Vehicles, Beijing Institute of Technology, Beijing, 100081, China

    Minxia Liu & Xintai Song

  2. American International University- Bangladesh, Dhaka, 1229, Bangladesh

    Mohammad Shidujaman

Authors
  1. Minxia Liu
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  2. Xintai Song
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  3. Mohammad Shidujaman
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Corresponding author

Correspondence to Xintai Song .

Editor information

Editors and Affiliations

  1. The Open University of Japan, Chiba, Japan

    Masaaki Kurosu

  2. Tokyo University of Science, Tokyo, Saitama, Japan

    Sakae Yamamoto

  3. Tokyo City University, Tokyo, Japan

    Hirohiko Mori

  4. Southern University of Science and Technology, Shenzhen, China

    Marcelo M. Soares

  5. World Usability Day and Bubble Mountain Consulting, Newton Center, MA, USA

    Elizabeth Rosenzweig

  6. Aaron Marcus and Associates, Berkeley, CA, USA

    Aaron Marcus

  7. Tsinghua University, Beijing, China

    Pei-Luen Patrick Rau

  8. Coventry University, Coventry, UK

    Don Harris

  9. Cranfield University, Cranfield, UK

    Wen-Chin Li

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Liu, M., Song, X., Shidujaman, M. (2022). A Study of Driver Fatigue States in Multiple Scenarios Based on the Fatigue and Sleepiness Indicator. In: Kurosu, M., et al. HCI International 2022 - Late Breaking Papers. Design, User Experience and Interaction. HCII 2022. Lecture Notes in Computer Science, vol 13516. Springer, Cham. https://doi.org/10.1007/978-3-031-17615-9_41

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  • DOI: https://doi.org/10.1007/978-3-031-17615-9_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-17614-2

  • Online ISBN: 978-3-031-17615-9

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