数据驱动建模的地面切向作用力估计
作者单位:

复旦大学


Data-Driven Modeling for Estimation of Tangential Ground Reaction Force
Affiliation:

Fudan University

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    摘要:

    目的 建立从下肢运动学数据估算切向地面反作用力(GRFt)的数据驱动模型,并从输入数量与估计精度的角度出发选择最合适的输入,以实现在户外步态实验中测量GRFt。方法 本文利用十名受试者在五个不同坡度(-10、-5、0、5、10°)下的步态数据,训练反向传播神经网络(BPNN)和多项式稀疏回归(PSI)模型等两种数据驱动模型,用以估算GRFt。本文评估了八种运动学数据组合与法向地面作用力(GRFn)作为模型输入的性能结果,以确定最佳的模型和模型输入。结果 在相同输入维度下,髋-膝关节角度的组合比膝-踝关节角度的组合更能准确地估算GRFt。具体而言,基于前者组合的BPNN和PSI模型预测GRFt的误差分别为1.61%和1.84%体重(BW),而基于后者组合的模型误差分别为2.82%和3.15% BW。此外,若将GRFn与所有关节角度作为输入,模型的预测误差仅为1.46%BW。结论 GRFn与髋-膝关节角度的组合在计算复杂度和估计精度之间实现了最佳平衡。本研究的结论有助于实现在室外步态测试中准确估算GRFt。

    Abstract:

    Objective To develop a data-driven model for estimating tangential ground reaction force (GRFt) from lower limb kinematic data and to select the most suitable input based on a balance between input quantity and estimation accuracy, with the aim of measuring GRFt in outdoor gait experiments. Methods Gait data from ten subjects walking at five different inclines (-10°, -5°, 0°, 5°, 10°) were used to train two data-driven models: a Backpropagation Neural Network (BPNN) and Polynomial Sparse Identification (PSI). The performance of these models was evaluated using eight kinematic data combinations and the normal ground reaction force (GRFn) as inputs to determine the optimal model and input combination. Results Under the same input dimensionality, the combination of hip-knee joint angles proved more accurate in estimating GRFt than the knee-ankle joint angle combination. Specifically, the BPNN and PSI models based on the former combination predicted GRFt with errors of 1.61% and 1.84% body weight (BW), respectively, while the latter combination resulted in errors of 2.82% and 3.15% BW. Moreover, when GRFn and all joint angles were used as inputs, the model’s prediction error was only 1.46% BW. Conclusion The combination of GRFn and hip-knee joint angles achieves an optimal balance between computational complexity and estimation accuracy. These findings support the accurate estimation of GRFt in outdoor gait testing.

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  • 收稿日期:2024-09-10
  • 最后修改日期:2024-10-06
  • 录用日期:2024-10-08
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