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Research on the Relationship between Reaction Ability and Mental State for Online Assessment of Driving Fatigue

Author

Listed:
  • Mengzhu Guo

    (School of Transportation, Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun 130022, China)

  • Shiwu Li

    (School of Transportation, Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun 130022, China)

  • Linhong Wang

    (School of Transportation, Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun 130022, China)

  • Meng Chai

    (School of Transportation, Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun 130022, China)

  • Facheng Chen

    (School of Transportation, Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun 130022, China)

  • Yunong Wei

    (School of Transportation, Jilin University, No. 5988, Renmin Street, Nanguan District, Changchun 130022, China)

Abstract

Background: Driving fatigue affects the reaction ability of a driver. The aim of this research is to analyze the relationship between driving fatigue, physiological signals and driver’s reaction time. Methods: Twenty subjects were tested during driving. Data pertaining to reaction time and physiological signals including electroencephalograph (EEG) were collected from twenty simulation experiments. Grey correlation analysis was used to select the input variable of the classification model. A support vector machine was used to divide the mental state into three levels. The penalty factor for the model was optimized using a genetic algorithm. Results: The results show that α/β has the greatest correlation to reaction time. The classification results show an accuracy of 86%, a sensitivity of 87.5% and a specificity of 85.53%. The average increase of reaction time is 16.72% from alert state to fatigued state. Females have a faster decrease in reaction ability than males as driving fatigue accumulates. Elderly drivers have longer reaction times than the young. Conclusions: A grey correlation analysis can be used to improve the classification accuracy of the support vector machine (SVM) model. This paper provides basic research that online detection of fatigue can be performed using only a simple device, which is more comfortable for users.

Suggested Citation

  • Mengzhu Guo & Shiwu Li & Linhong Wang & Meng Chai & Facheng Chen & Yunong Wei, 2016. "Research on the Relationship between Reaction Ability and Mental State for Online Assessment of Driving Fatigue," IJERPH, MDPI, vol. 13(12), pages 1-15, November.
    • Handle: RePEc:gam:jijerp:v:13:y:2016:i:12:p:1174-:d:83646
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    Citations

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    Cited by:

    1. Al-Baraa Abdulrahman Al-Mekhlafi & Ahmad Shahrul Nizam Isha & Nicholas Chileshe & Mohammed Abdulrab & Anwar Ameen Hezam Saeed & Ahmed Farouk Kineber, 2021. "Modelling the Relationship between the Nature of Work Factors and Driving Performance Mediating by Role of Fatigue," IJERPH, MDPI, vol. 18(13), pages 1-17, June.
    2. Jurijus Zaranka & Robertas Pečeliūnas & Vidas Žuraulis, 2021. "A Road Safety-Based Selection Methodology for Professional Drivers: Behaviour and Accident Rate Analysis," IJERPH, MDPI, vol. 18(23), pages 1-18, November.

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