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Machine learning evaluation model of pilot workload in a low-visibility environment.

Yuansheng Wang1,2, Xinyao Guo3,4, Shaoshuai Guo1,2

  • 1School of Environmental and Municipal Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China.

Scientific Reports
|July 2, 2025
PubMed
Summary

Pilots experience increased workload in low visibility, evidenced by higher heart rates and NASA-TLX scores. A machine learning model using ECG data accurately assesses pilot workload, improving flight safety.

Keywords:
Civil pilotsECG signalLow visibilityMachine learningWorkload

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Area of Science:

  • Aviation Psychology
  • Biomedical Engineering
  • Machine Learning

Background:

  • Pilot workload assessment is critical for flight safety, especially in low-visibility conditions.
  • Existing methods may not fully capture the physiological and subjective aspects of workload.
  • Understanding workload variations aids in developing better safety protocols.

Purpose of the Study:

  • To analyze pilot workload trends in low-visibility flight.
  • To develop a quantitative evaluation method for pilot workload.
  • To identify sensitive physiological indicators of workload.

Main Methods:

  • Utilized an E01-pro simulated flight platform and PhysioPlux tester to collect ECG data from 40 pilots.
  • Monitored pilots in normal and low-visibility environments, collecting ECG signals and NASA-TLX workload scale data.
  • Employed machine learning, specifically the hidden Markov model (HMM), integrating ECG indexes and subjective data.

Main Results:

  • Pilot average heart rate (HR) and NASA-TLX scores significantly increased in low-visibility conditions.
  • ECG indexes pNN20, HF/LF, SD2/SD1, and HR showed significant differences across workload levels.
  • The HMM-based workload evaluation model achieved an accuracy of 87.5%.

Conclusions:

  • Low visibility significantly impacts pilot physiological and subjective workload.
  • Specific ECG-derived heart rate variability (HRV) indexes are sensitive to workload changes.
  • The developed HMM model offers a reliable method for rapid pilot workload assessment.