A trained neural network model accurately predicts Achilles tendon stress during walking and running based on shear wave propagation.
A trained neural network model accurately predicts Achilles tendon stress during walking and running based on shear wave propagation.
复制标题
经过训练的神经网络模型根据剪切波传播准确预测步行和跑步期间的跟腱应力。
DOI：
10.1016/j.jbiomech.2023.111699
复制
发表时间：
2023
影响因子：
2.4
通讯作者：
Thelen,DarrylG
中科院分区：
文献类型：
--
作者：
Martin,JackA;Thelen,DarrylG
剪切波张力计是一种基于横波沿肌腱传播的速度来测量活动期间肌腱负荷的非侵入性技术。剪切波速已被证明与轴向应力调制，但需要校准才能获得肌腱载荷的绝对测量。然而，目前的技术只利用波速，而波的其他特征(例如，幅度、频率含量)也可能随着肌腱载荷的变化而变化。除了波速之外，还可以使用这些数据来规避校准的需要。考虑到肌腱负荷的潜在复杂关系，以及缺乏指导这些数据使用的分析模型，使用机器学习方法是明智的。在这里，我们使用集成神经网络方法从先前研究中收集的剪切波张力测量数据预测跟腱应力的逆动力学估计。神经网络预测的压力与行走(R2=100.89±100.06)和跑步(R2=100.87±100.11)的站立位相逆动力学估计高度相关，这些数据保留用于神经网络模型测试，不包括在模型训练中。此外，神经网络预测的应力与逆动力学估计的应力之间的误差是合理的(步行：RMSD=峰值负荷的11%±22.2%；跑步：25%±114%)。这一初步分析的结果表明，机器学习方法可以减少横波张力计对校准的依赖，并在许多情况下扩大其可用性。
Shear wave tensiometry is a noninvasive technique for measuring tendon loading during activity based on the speed of a shear wave traveling along the tendon. Shear wave speed has been shown to modulate with axial stress, but calibration is required to obtain absolute measures of tendon loading. However, the current technique only makes use of wave speed, whereas other characteristics of the wave (e.g., amplitude, frequency content) may also vary with tendon loading. It is possible that these data could be used in addition to wave speed to circumvent the need for calibration. Given the potential complex relationships to tendon loading, and the lack of an analytical model to guide the use of these data, it is sensible to use a machine learning approach. Here, we used an ensemble neural network approach to predict inverse dynamics estimates of Achilles tendon stress from shear wave tensiometry data collected in a prior study. Neural network-predicted stresses were highly correlated with stance phase inverse dynamics estimates for walking (R2= 0.89 ± 0.06) and running (R2= 0.87 ± 0.11) data reserved for neural network model testing and not included in model training. Additionally, error between neural network-predicted and inverse dynamics-estimated stress was reasonable (walking: RMSD = 11 ± 2% of peak load; running: 25 ± 14%). Results of this pilot analysis suggest that a machine learning approach could reduce the reliance of shear wave tensiometry on calibration and expand its usability in many settings.