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In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
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从单个IMU中基于深度学习的关节角度估计中的个体变量:跨人群研究

Koyo Toyoshima1, Jae Hoon Lee1, Shigeru Kogami2

  • 1Graduate School of Science and Engineering, Ehime University, Bunkyo-cho 3, Matsuyama 790-8577, Ehime, Japan.

Sensors (Basel, Switzerland)
|January 10, 2026
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概括

深度学习从单个IMU传感器准确地估计了关节角度,改善了老年人的步态分析. 跨人群培训的有效性随着步态异质而变化,影响临床应用.

关键词:
深度学习是一种深度学习.步态分析 步态分析概括的概括是一般化的.关节骨关节炎是一种骨关节炎.个体的变化,个体的变化.惯性测量单位是一种惯性测量单位.关节角度估计 关节角度估计年龄较大的成年人.可以穿戴的传感器.

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科学领域:

  • 生物力学 生物力学
  • 机器学习 机器学习
  • 老年学是指老年学的学科.

背景情况:

  • 关节角度测量对于步态分析至关重要,但复杂的运动捕捉限制了临床使用.
  • 可穿戴惯性测量单元 (IMU) 为步态评估提供了一个更简单的替代方案.
  • 深度学习模型显示出从IMU数据估计关节动力学的前景.

研究的目的:

  • 研究深度学习模型的可通用性,用于使用单个骨盆IMU来估计关节角度.
  • 为了进行步态分析,比较人口内部与跨人口培训策略.
  • 评估特定人群的步态特征对模型性能的影响.

主要方法:

  • 收集了年轻成年人,健康的老年人和关节骨关节炎患者的步态数据.
  • 训练了一个1D ResNet卷积神经网络,从IMU信号中估计部,膝盖和脚关节的角度.
  • 雇员嵌入了5倍交叉验证,以比较人口内部和跨人口培训方法.

主要成果:

  • 跨人群培训显著改善了老年人的步态分析.
  • 由于基线表现高,年轻人表现的改善很小.
  • 手术前的患者对跨人群训练的反应非常可变,突出显示了步态异质性.

结论:

  • 在步态分析中跨人群学习的有效性受到人群内步态变化的影响.
  • 这些发现对开发针对不同人群的强大,临床适用的步态分析系统具有重要意义.
  • 基于单一IMU的深度学习为客观步态评估提供了可扩展的方法.