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运营商EYEVP:基于眼动,心率数据和视频信息的疲劳检测运营商数据集.

Svetlana Kovalenko1, Anton Mamonov2, Vladislav Kuznetsov3

  • 1Institute of Cognitive Neuroscience, HSE University, Moscow 101000, Russia.

Sensors (Basel, Switzerland)
|July 14, 2023
PubMed
概括

这项研究引入了用于疲劳检测的新数据集,这对于事故预防系统至关重要. 分析显示,眼睛跟踪数据与选择反应时间相关,表明疲劳存在.

关键词:
HRV (心率变化) 是指心率的变化.数据集数据集数据集眼睛跟踪 眼睛跟踪面对面和头部视频视频疲劳 疲劳 疲劳 疲劳 疲劳 疲劳凝视跟踪 追踪 追踪 追踪

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科学领域:

  • 用于检测疲劳的生理和行为指标.
  • 开发预防系统,防止事故发生.

背景情况:

  • 疲劳检测对于诸如驾驶员和操作员监控等系统至关重要.
  • 需要客观的生理和行为指标来准确评估疲劳.
  • 现有的公共数据集缺乏用于疲劳检测模型开发的全面数据.

研究的目的:

  • 为疲劳检测研究创建一个新的数据集.
  • 确定可靠的生理和行为疲劳指标.
  • 评估现有数据集对于疲劳检测的适用性.

主要方法:

  • 在8天内从10名参与者收集了多模式数据 (眼睛跟踪,视频,心率).
  • 参与者从事模拟日常活动的各种任务 (例如,选择反应时间,阅读).
  • 分析了公共数据集和新收集的数据,以检测疲劳的适用性.

主要成果:

  • 现有的公共数据集被认为不足以全面检测疲劳.
  • 新记录的数据集证明了对疲劳研究的有用性.
  • 眼睛跟踪数据和选择反应时间之间的相关性表明疲劳存在.

结论:

  • 成功创建了一个新的多模式数据集,用于疲劳检测.
  • 该数据集支持识别疲劳指标.
  • 眼睛跟踪数据显示,与行为任务相结合,作为疲劳指标具有前景.