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Updated: Jul 10, 2025

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基于可穿戴传感器的上肢疲劳估计的数据驱动方法.

Sophia Otálora1, Marcelo E V Segatto1, Maxwell E Monteiro2

  • 1Telecommunications Laboratory (LabTel), Electrical Engineering Department, Federal University of Espírito Santo (UFES), Vitória 290075-910, Brazil.

Sensors (Basel, Switzerland)
|November 25, 2023
PubMed
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这项研究引入了一种可穿戴式传感器模型,用于检测重复举重任务期间的肌肉疲劳. 该模型准确地估计了疲劳水平,有助于预防与工作相关的肌肉骨疾病.

科学领域:

  • 生物力学 生物力学
  • 可穿戴技术可穿戴技术
  • 机器学习 机器学习

背景情况:

  • 肌肉疲劳会阻碍员工的表现和幸福感,特别是在重复性任务中.
  • 传统的电肌学 (EMG) 对职业环境中的长期监测有局限性.
  • 可穿戴,非侵入性设备为评估肌肉疲劳提供了切实可行的替代方案.

研究的目的:

  • 开发和验证一个计算模型,用可穿戴传感器来估计肌肉疲劳.
  • 为了比较不同传感器组合 (光纤传感器 - OFS,惯性测量单元 - IMU,EMG) 对于疲劳检测的有效性.
  • 为了识别肌肉疲劳的关键生物力学特征.

主要方法:

  • 30名受试者进行了重复的手臂举动任务,直到疲劳.
  • 收集的数据包括肌肉活动 (EMG),肘部角度,速度和主观的博格尺度等级.
  • 机器学习算法,包括LightGBM,用于分类疲劳状态 (低,中等,高).

主要成果:

  • 使用所有传感器和33个功能,LightGBM模型实现了96.2%的准确性.
  • 仅使用OFS和IMU传感器 (13个特征) 的模型达到95.4%的精度.
  • 疲劳估计的关键特征包括肘部角度,手腕速度,加速度变化和部运动.
关键词:
光纤传感器 光纤传感器电动肌谱学 电动肌谱学惯性传感器 惯性传感器机器学习是机器学习.肌肉疲劳 肌肉疲劳 肌肉疲劳

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结论:

  • 使用OFS和IMU传感器的计算模型可以有效地估计在举重时的肌肉疲劳.
  • 该模型有可能用于监测和预防职业环境中的肌肉疲劳.
  • 具体的生物力学指标对于准确的疲劳评估至关重要.