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通过CNN编码解码模型从多个生物信号进行增强的驾驶员压力预测.

Vishal Singh Roha, Maggie Ezzat Gaber Gendy, Kaveti Pavan

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    通过使用多式联络生物信号,PhysioFuseNet 增强了驾驶员应激分类. 这种新的框架在识别认知和情绪压力方面取得了很高的准确性,为实时压力评估提供了潜力.

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    科学领域:

    • 生理学 生理学 生理学
    • 机器学习 机器学习
    • 人与计算机的交互

    背景情况:

    • 司机的压力会对道路安全产生负面影响.
    • 准确的压力检测对于开发有效的驾驶员支持系统至关重要.
    • 现有的方法在分类各种压力类型时往往缺乏稳定性.

    研究的目的:

    • 引入PhysioFuseNet,这是驾驶员压力状态分类的新型框架.
    • 评估多模式生物信号融合在应力检测方面的有效性.
    • 在PhysioFuseNet框架内比较不同机器学习分类器的性能.

    主要方法:

    • 在驾驶模拟器中从25名健康受试者获得多式生物信号 (心电图,EDA,PPG,呼吸).
    • 使用基于CNN的编码解码器模型进行特征提取.
    • 使用提取特征的中间融合.
    • 使用支持矢量机 (SVM) 和随机森林 (RF) 算法进行分类.

    主要成果:

    • 物理FuseNet实现了高分类准确度:SVM的0.95和RF的0.94.
    • 与以前的方法相比,该框架显示出更高的性能.
    • 该系统在分类情绪和认知压力状态方面表现特别强.

    结论:

    • 物理FuseNet为驾驶员压力分类提供了强大而有效的方法.
    • 多模式生物信号融合显著提高了应激检测的准确性.
    • 拟议的框架对临床和现实驾驶场景中的实时压力评估应用具有前景.