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A Feature Fusion Method for Driving Fatigue of Shield Machine Drivers Based on Multiple Physiological Signals and Auto-Encoder

Author

Listed:
  • Kun Liu

    (School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China
    College of Business Administration, Northeast University, Shenyang 110819, China)

  • Guoqi Feng

    (College of Business Administration, Northeast University, Shenyang 110819, China)

  • Xingyu Jiang

    (School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Wenpeng Zhao

    (School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Zhiqiang Tian

    (School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Rizheng Zhao

    (School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China)

  • Kaihang Bi

    (School of Mechanical Engineering, Shenyang University of Technology, Shenyang 110870, China)

Abstract

The driving fatigue state of shield machine drivers directly affects the safe operation and tunneling efficiency of shield machines during metro construction. To cope with the problem that it is challenging to simulate the working conditions and operation process of shield machine drivers using driving simulation platforms and that the existing fatigue feature fusion methods usually show low recognition accuracy, shield machine drivers at Shenyang metro line 4 in China were taken as the research subjects, and a multi-modal physiological feature fusion method based on an L2-regularized stacked auto-encoder was designed. First, the ErgoLAB cloud platform was used to extract the combined energy feature (E), the reaction time, the HRV (heart rate variability) time-domain SDNN (standard deviation of normal-to-normal intervals) index, the HRV frequency-domain LF/HF (energy ratio of low frequency to high frequency) index and the pupil diameter index from EEG (electroencephalogram) signals, skin signals, pulse signals and eye movement data, respectively. Second, the physiological signal characteristics were extracted based on the WPT (wavelet packet transform) method and time–frequency analysis. Then, a method for driving fatigue feature fusion based on an auto-encoder was designed aiming at the characteristics of the L2-regularization method to solve the over-fitting problem of small sample data sets in the process of model training. The optimal hyper-parameters of the model were verified with the experimental method of the control variable, which reduces the loss of multi-modal feature data in compression fusion and the information loss rate of the fused index. The results show that the method proposed outperforms its competitors in recognition accuracy and can effectively reduce the loss rate of deep features in existing decision-making-level fusion.

Suggested Citation

  • Kun Liu & Guoqi Feng & Xingyu Jiang & Wenpeng Zhao & Zhiqiang Tian & Rizheng Zhao & Kaihang Bi, 2023. "A Feature Fusion Method for Driving Fatigue of Shield Machine Drivers Based on Multiple Physiological Signals and Auto-Encoder," Sustainability, MDPI, vol. 15(12), pages 1-25, June.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:12:p:9405-:d:1168943
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    References listed on IDEAS

    as
    1. Xiangbing Zhao & Jianhui Zhou & Xiaofeng Li, 2022. "Fast Recognition Algorithm for Human Motion Posture Using Multimodal Bioinformation Fusion," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-9, April.
    2. Zuojin Li & Qing Yang & Shengfu Chen & Wei Zhou & Liukui Chen & Lei Song, 2019. "A fuzzy recurrent neural network for driver fatigue detection based on steering-wheel angle sensor data," International Journal of Distributed Sensor Networks, , vol. 15(9), pages 15501477198, September.
    3. Wei Sun & Xiaorui Zhang & Jian Wang & Jun He & Srinivas Peeta, 2015. "Blink Number Forecasting Based on Improved Bayesian Fusion Algorithm for Fatigue Driving Detection," Mathematical Problems in Engineering, Hindawi, vol. 2015, pages 1-13, June.
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