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相关概念视频

Fatigue01:21

Fatigue

190
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...
190

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相关实验视频

Updated: Jul 15, 2025

The Treadmill Fatigue Test: A Simple, High-throughput Assay of Fatigue-like Behavior for the Mouse
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一种基于数据的方法来检测跑步过程中的疲劳,使用小儿测试仪进行测量.

Zixiang Gao1,2,3, Liangliang Xiang1,4, Gusztáv Fekete3

  • 1Department of Radiology, Ningbo No. 2 Hospital, Ningbo 315010, China.

Applied bionics and biomechanics
|October 5, 2023
PubMed
概括

早期发现跑步疲劳可以预防过度使用的伤害. 这项研究发现,疲劳后脚下部力量分布发生了显著的变化,深度学习模型准确地识别了疲劳的步态.

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相关实验视频

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科学领域:

  • 生物力学和体育科学 生物力学和体育科学
  • 运动中的机器学习
  • 步态分析 步态分析

背景情况:

  • 早期发现跑步疲劳对于预防过度使用伤害至关重要.
  • 研究疲劳对业余跑步者的脚部足部力分布的影响.

研究的目的:

  • 分析跑步疲劳如何影响主导和非主导四肢的脚下力量分布.
  • 开发和评估用于自动识别疲劳步态的深度学习模型.

主要方法:

  • 30名业余跑步者接受了疲劳检查.
  • 双边时间序列的足部力被测量使用一个脚扫描板在疲劳之前和之后.
  • 使用Python训练了基于CNN的卷积神经网络 (CNN) 和基于CNN的长期短期记忆 (ConvLSTM) 模型.

主要成果:

  • 疲劳改变了脚下力量的分布,增加了主导肢体中部前脚和脚跟的力量.
  • 峰值力时刻在中足/总和 (非主导) 和半足 (主导) 区域都发生了转移.
  • 与CNN (0.800) 相比,ConvLSTM模型在疲劳步态检测方面取得了更高的准确性 (0.867).

结论:

  • 结果提供了数据来评估过度使用的伤害危险因素在单肢.
  • 通过脚部力分析和深度学习,可以早期检测疲劳的步态.