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使用可解释的机器学习验证和解释一个多式模式的昏昏欲睡检测系统.

Md Mahmudul Hasan1, Christopher N Watling2, Grégoire S Larue3

  • 1School of Computer Science and Engineering, University of New South Wales (UNSW), Australia; Centre for Accident Research and Road Safety - Queensland (CARRS-Q), Queensland University of Technology (QUT), Australia.

Computer methods and programs in biomedicine
|November 24, 2023
PubMed
概括
此摘要是机器生成的。

这项研究开发了一个可靠的昏昏欲睡的驾驶检测系统,使用生理信号和可解释的机器学习. 随机森林模型实现了高精度,为道路安全提供了可靠和可解释的解决方案.

关键词:
功能 功能 功能 功能可以解释性 解释性部分依赖性分析生理信号 生理信号在SHAP分析中,我们分析了SHAP.验证 验证 验证 验证

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科学领域:

  • 神经科学是一个神经科学.
  • 机器学习 机器学习
  • 道路安全工程 道路安全工程

背景情况:

  • 昏昏欲睡的驾驶是一个重要的道路安全危险.
  • 现有的检测系统经常充当"黑子",缺乏稳定性和可解释性.
  • 需要可靠的机器学习模型来检测昏昏欲睡的驾驶.

研究的目的:

  • 通过使用多种技术,严格验证昏睡驾驶检测系统.
  • 通过可解释的机器学习来提高模型可信度.
  • 解释有助于检测昏昏欲睡的生理信号.

主要方法:

  • 模拟驾驶任务与生理信号记录 (EEG,EOG,ECG).
  • 应用主体依赖和独立的验证技术.
  • 使用K-最近邻居,支向量机器和随机森林分类器.
  • 在模型解释方面使用了夏普利添加式扩展 (SHAP) 和部分依赖性分析 (PDA).

主要成果:

  • 独立于对象的验证 (将一名参与者排除在外) 证明最有效.
  • 随机森林分类器实现了70.3%的灵敏度,82.2%的特异性和80.1%的准确性.
  • 可解释的方法确定了用于检测昏昏欲睡的关键生理特征.

结论:

  • 该研究为可靠的嗜睡检测系统提供了强有力的验证和可解释的方法.
  • 可解释的机器学习提高了车载系统的可靠性和可解释性.
  • 这种方法有望提高道路安全,并降低系统成本.