基于飞行员运动行为与脑电信息融合的疲劳飞行特征检测与分析。

Li Ji, Leiye Yi, Haiwei Li, Wenjie Han, Ningning Zhang
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引用次数: 0

摘要

目的:飞行员在飞行过程中容易疲劳,对飞行安全构成重大风险。然而,基于单一特征的检测方法往往缺乏准确性和鲁棒性。方法:提出一种结合脑电特征和运动行为特征的疲劳分类方法,以增强疲劳识别,提高航空安全。该方法提取脑电信号各频段(α、β、θ、δ)的能量比,结合前臂样本熵和欧拉角标准差,应用Pearson相关分析选择关键特征。最后,采用支持向量机分类器对疲劳进行精确分类。结果:实验结果表明,该方法的测试精度为93.67 %,优于现有的疲劳检测技术,同时降低了计算成本。结论:本研究通过整合生理和行为数据进行疲劳分类,解决了目前研究的空白,表明与单一特征方法相比,多源信息融合显著提高了检测精度和稳定性。研究结果有助于通过提高疲劳监测系统的可靠性来提高飞行员的性能和飞行安全。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection and analysis of fatigue flight features using the fusion of pilot motion behavior and EEG information.

Objectives: Pilots are susceptible to fatigue during flight operations, posing significant risks to flight safety. However, single-feature-based detection methods often lack accuracy and robustness.

Methods: This study proposes a fatigue classification approach that integrates EEG features and motion behavior features to enhance fatigue recognition and improve aviation safety. The method extracts energy ratios of EEG frequency bands (α, β, θ, δ), incorporates forearm sample entropy and Euler angle standard deviation, and applies Pearson correlation analysis to select key features. Finally, a Support Vector Machine (SVM) classifier is employed to achieve precise fatigue classification.

Results: Experimental findings indicate that the proposed method achieves a test accuracy of 93.67 %, outperforming existing fatigue detection techniques while operating with a reduced computational cost.

Conclusions: This study addresses a gap in current research by integrating physiological and behavioral data for fatigue classification, demonstrating that the fusion of multi-source information significantly enhances detection accuracy and stability compared to single-feature methods. The findings contribute to improved pilot performance and enhanced flight safety by increasing the reliability of fatigue monitoring systems.

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