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
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.
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.
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