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A Practical Application for Quantitative Brain Fatigue Evaluation Based on Machine Learning and Ballistocardiogram.

Yanting Xu1,2, Zhengyuan Yang1,2, Gang Li1,2,3

  • 1Key Laboratory of Intelligent Operation and Maintenance Technology and Equipment for Urban Rail Transit of Zhejiang Province, Jinhua 321004, China.

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|November 27, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a non-contact method using ballistocardiogram (BCG) signals and random forest machine learning to accurately detect brain fatigue. The system achieved 96.54% accuracy, offering a new tool for real-time monitoring and mental health assessment.

Keywords:
ballistocardiogram (BCG)brain fatiguefiber-optic sensorheart rate variability (HRV)machine learningmental health

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Area of Science:

  • Biomedical Engineering
  • Cognitive Science
  • Machine Learning

Background:

  • Brain fatigue negatively impacts cognitive function, work efficiency, and mental health.
  • Accurate and practical methods for quantifying brain fatigue are crucial.
  • Non-contact monitoring methods are desirable for continuous assessment.

Purpose of the Study:

  • To develop and validate a non-contact method for quantifying brain fatigue using ballistocardiogram (BCG) signals.
  • To assess the effectiveness of a random forest machine learning model in identifying brain fatigue states.
  • To explore the correlation between heart rate variability (HRV) and brain fatigue levels.

Main Methods:

  • Collected non-contact BCG signals using an optical fiber sensor cushion during cognitive tasks in 20 subjects.
  • Calculated heart rate variability (HRV) from BCG signals.
  • Developed a random forest machine learning classifier to detect and quantify brain fatigue.

Main Results:

  • Heart rate derived from BCG signals showed high consistency with medical equipment (mean error ±0.81 bpm).
  • The random forest classifier achieved 96.54% accuracy in identifying brain fatigue.
  • A strong correlation (0.98) was found between HRV and accuracy, indicating HRV's utility for quantitative fatigue evaluation.

Conclusions:

  • The developed non-contact method effectively quantifies brain fatigue using BCG and machine learning.
  • This technology can enable real-time, non-disturbing brain fatigue detection.
  • Potential applications include improving safety in human-machine interaction and enhancing mental well-being for workers and drivers.