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Prediction Method of Human Fatigue in an Artificial Atmospheric Environment Based on Dynamic Bayesian Network

Author

Listed:
  • Liping Pang

    (School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China)

  • Pei Li

    (School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China)

  • Liang Guo

    (School of Aeronautic Science and Engineering, Beihang University, Beijing 100191, China)

  • Xin Wang

    (Marine Human Factors Engineering Lab, China Institute of Marine Technology & Economy, Beijing 100081, China)

  • Hongquan Qu

    (School of Information Science and Technology, North China University of Technology, Beijing 100144, China)

Abstract

Fatigue state usually leads to slow reaction of the human body and its thoughts. It is an important factor causing significant decline in the working ability of workers, an increase in error rate and even major accidents. It would have a more negative impact in an artificial atmospheric environment. The effective prediction of fatigue can contribute to improved working efficiency and reduce the occurrence of accidents. In this paper, a prediction method of human fatigue in an artificial atmospheric environment was established, combining as many as eight input parameters about the cause and effect of human fatigue based on a dynamic Bayesian network in order to achieve a relatively comprehensive and accurate prediction of human fatigue. This fatigue prediction method was checked by experimental results. The results indicate that the established prediction method could provide a relatively reliable way to predict a worker fatigue state in an artificial atmospheric working environment.

Suggested Citation

  • Liping Pang & Pei Li & Liang Guo & Xin Wang & Hongquan Qu, 2022. "Prediction Method of Human Fatigue in an Artificial Atmospheric Environment Based on Dynamic Bayesian Network," Mathematics, MDPI, vol. 10(15), pages 1-13, August.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:15:p:2778-:d:880946
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    References listed on IDEAS

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    1. Jiang, Kang & Ling, Feiyang & Feng, Zhongxiang & Wang, Kun & Shao, Cheng, 2017. "Why do drivers continue driving while fatigued? An application of the theory of planned behaviour," Transportation Research Part A: Policy and Practice, Elsevier, vol. 98(C), pages 141-149.
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