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相关概念视频

Substance Use Disorders Affecting Sleep01:24

Substance Use Disorders Affecting Sleep

471
Substance use disorders involve a pattern of using drugs more extensively than intended and continuing use despite harmful consequences. This includes legal substances like alcohol and nicotine, as well as illegal drugs. These disorders often involve both physical and psychological dependence, reflecting compulsive use of substances that significantly alter thoughts, feelings, and behaviors, contributing to a major public health issue.
Understanding the concepts of physical dependence,...
471

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相关实验视频

Updated: Feb 26, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

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一个使用视频,生物识别和行为数据的多式睡眠数据集.

Morteza Bodaghi1, Majid Hosseini1, Raju Gottumukkala2

  • 1University of Louisiana at LAfayette, lafayette, LA, USA.

Scientific data
|February 24, 2026
PubMed
概括
此摘要是机器生成的。

这项研究引入了一套新的公共数据集,用于检测驾驶员的昏昏欲睡,将面部,行为和生物识别数据结合起来. 数据集在40分钟的时间内捕捉了逐渐的嗜睡变化,为驾驶员状态提供了更丰富的理解.

更多相关视频

Multi-Modal Home Sleep Monitoring in Older Adults
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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
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Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

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相关实验视频

Last Updated: Feb 26, 2026

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

11.2K
Multi-Modal Home Sleep Monitoring in Older Adults
07:40

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

8.3K
Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
08:36

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

12.9K

科学领域:

  • 多式联网驾驶员监控多式联网驾驶员监控
  • 生物识别信号分析
  • 人与计算机在车辆中的互动.

背景情况:

  • 司机昏昏欲睡是一个主要的安全问题,导致事故.
  • 现有的数据集往往缺乏多式联运集成或持续状态监控.
  • 准确的检测需要全面的生理和行为数据.

研究的目的:

  • 引入一个全面的,公开可用的多式联运数据集,用于检测驾驶员的昏昏欲睡.
  • 为了捕捉与昏昏欲睡相关的持续生理和行为变化.
  • 促进研究先进的驾驶辅助系统和道路安全.

主要方法:

  • 在1400分钟内从19名受试者收集了多式联络数据.
  • 包括3D面部视频,红外线,姿势,心率,电皮活动,血液氧气,皮肤温度,加速度计,握力和遥测数据.
  • 使用卡罗林斯卡睡眠度量表 (KSS) 进行持续的嗜睡自我报告.

主要成果:

  • 数据集包括每个主题连续40分钟的会议,共1400分钟.
  • 捕获了警觉和昏昏欲睡状态之间的逐渐过渡,而不仅仅是离散的标签.
  • 集成多种信号,包括面部表情,姿势和生理指标.

结论:

  • 这一数据集为开发强大的驾驶员昏昏欲睡检测系统提供了宝贵的资源.
  • 持续监控和多式联运集成提供了更现实的驾驶员状态的表现.
  • 这一数据集将推动实时驾驶员安全和自动驾驶技术的研究.