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相关概念视频

Muscle Recovery and Fatigue01:24

Muscle Recovery and Fatigue

2.0K
Muscle fatigue refers to the decline in a muscle's ability to maintain the force of contraction after prolonged activity. It primarily stems from changes within muscle fibers. Even before experiencing muscle fatigue, one may feel tired and have the urge to stop the activity. This response, known as central fatigue, occurs due to changes in the central nervous system, namely the brain and spinal cord. While there is no single mechanism that induces fatigue, it may serve as a protective...
2.0K
Fatigue01:21

Fatigue

174
Fatigue occurs when materials rupture under repeated or fluctuating loads, even at stress levels far below their static breaking strength. It typically results in brittle failure, even for ductile materials. It is a critical consideration in designing machines and structural components subjected to repetitive or varying loads. The nature of these loadings can range from fluctuating loads like unbalanced pump impellers causing vibrations to repeatedly bending a thin steel rod wire back and forth...
174
Survival Tree01:19

Survival Tree

61
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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相关实验视频

Updated: Jun 9, 2025

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
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使用基于XGBoost算法的生理数据识别登工人的疲劳.

Yonggang Xu1, Qingzhi Jian2, Kunshuang Zhu1

  • 1Emergency Management Center of State Grid Shandong Electric Power Company, Jinan, China.

Frontiers in public health
|October 24, 2024
PubMed
概括
此摘要是机器生成的。

高压工作人员高压工作人员

关键词:
在XGBoost中使用.登工人 登工人疲劳识别 疲劳识别 疲劳识别机器学习是机器学习.生理学数据 生理学数据

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科学领域:

  • 职业健康 职业健康 职业健康
  • 身体生理学 身体生理学
  • 人体工程学就是人体工程学.

背景情况:

  • 高压工作涉及体力苛刻的登任务.
  • 这些工人的疲劳会损害运动和认知功能,造成安全风险.
  • 有效的疲劳监测对于工人的安全至关重要.

研究的目的:

  • 开发一种实验方法来量化爬过程中高压工人的疲劳.
  • 确定用于评估登引起的疲劳的关键生理指标.
  • 使用机器学习创建一个预测性疲劳识别模型.

主要方法:

  • 收集了33名高压工人的主观 (RPE尺度) 和客观 (SBP,DBP,SpO2,VC,GS,RT,CFF,HR) 疲劳数据.
  • 执行登任务以诱导疲劳.
  • 利用XGBoost算法构建一个疲劳识别模型.

主要成果:

  • 血液中氧和度 (SpO2),生命能力 (VC),握力 (GS),反应时间 (RT) 和临界融合频率 (CFF) 被确定为有效的疲劳指标.
  • 结合主观疲劳和这五个生理指标的XGBoost模型在疲劳识别中实现了89.75%的准确性.

结论:

  • 该研究建立了一种可靠的方法,用于评估高压工人在登过程中的疲劳.
  • 研究结果支持个性化的疲劳管理策略,以提高工人安全.
  • 及时检测疲劳可以减轻在苛刻的职业环境中的意外风险.