在各种运动任务中预测3D地面反应力:一个卷积神经网络研究,比较不同的惯性测量单元配置
Batın Yılmazgün1, Jonas Weber1, Thorsten Stein1
1Institute of Sports and Sports Science, Karlsruhe Institute of Technology, Karlsruhe, Germany.
Journal of biomechanics
|September 19, 2025
概括
卷积神经网络 (CNN) 使用惯性测量单位 (IMU) 准确预测3D地面反应力 (GRF). 单个IMU设置对于垂直GRF是有效的,而多个IMU配置提高了切割运动的准确性.
科学领域:
- 生物力学 生物力学
- 机器学习 机器学习
- 可穿戴技术可穿戴技术
背景情况:
- 地面反应力 (GRF) 对于分析运动和肌肉骨负荷至关重要.
- 惯性测量单元 (IMU) 允许在现实环境中进行步态分析,但不能直接测量GRF.
- 机器学习为从IMU数据中估计3D-GRF提供了一个有希望的方法,但研究主要集中在垂直GRF和有限运动类型上.
研究的目的:
- 系统地评估卷积神经网络 (CNN) 对3D-GRF的预测准确性.
- 用IMU从各种传感器配置 (单个和多个) 来评估预测性能.
- 分析各种运动任务的准确性,包括步行,楼梯谈判和切割机动.
主要方法:
- 20名健康参与者以自己选择的速度执行了6个不同的运动任务.
- 通过IMU时间序列数据,CNN被训练来预测3D-GRF,这些数据来自包括下体 (7个IMU),单脚 (4个IMU),大腿骨 (2个IMU),大腿骨 (1个IMU) 和骨盆 (1个IMU) 在内的配置.
- 预测的准确性是使用leave-one-subject-out交叉验证,使用皮尔森相关性 (r) 和相对根平均平方误差 (relRMSE) 来评估的.
主要成果:
- 在所有任务中,CNN在预测垂直GRF (vGRF) (r=0.98,relRMSE ≤ 7.44%) 中表现出高准确度.
- 前后GRF (apGRF) 的预测也很准确 (r ≥ 0.92,relRMSE ≤ 14.24%),而中侧GRF (mlGRF) 的预测不那么准确 (r ≥ 0.74,relRMSE ≤ 29.46%).
- 在多IMU和单IMU配置之间,vGRF预测准确性是一致的,支持使用更简单的设置来估计vGRF. 多个IMU配置显示在切割机动中,mlGRF和apGRF的精度得到了提高.
结论:
- 在各种运动中,CNN有效地从IMU数据中预测3D-GRF.
- 单个IMU配置足以准确预测vGRF,提供实用优势.
- 先进的传感器配置 (例如,底身) 在复杂的动态任务 (如切割机动) 中提高apGRF和mlGRF的预测准确度,表明传感器简单性和预测性能的权衡.
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