Estimating Knee Joint Load Using Acoustic Emissions During Ambulation.
Estimating Knee Joint Load Using Acoustic Emissions During Ambulation.
复制标题
使用行走过程中的声发射估计膝关节负荷。
DOI：
10.1007/s10439-020-02641-7
复制
发表时间：
2021
影响因子：
3.8
通讯作者：
Young,AaronJ
中科院分区：
文献类型：
--
作者：
Scherpereel,KeatonL;Bolus,NicholasB;Jeong,HyeonKi;Inan,OmerT;Young,AaronJ
量化日常生活活动中的联合负荷可以改善许多人的行动能力；然而，目前评估联合负荷的方法不适用于无处不在的环境。这项研究的目的是证明，在潜在的可穿戴装置中，联合声发射包含估计这种内部联合载荷的信息。11名健康、健全的受试者在不同的速度、倾斜和负荷条件下执行行走任务，同时收集关节声发射和基本步态测量-肌电图、地面反作用力和运动捕捉轨迹-。步态测量使用神经肌肉模型来估计内部关节接触力，这是基于关节声发射的频谱、时间、倒谱和幅度特征训练的特定主题机器学习模型(XGBoost)的目标变量。使用联合声发射的模型在可见(MAE=0.08±0.01BW)和不可见(MAE=0.21±0.05BW)两种情况下，显著优于(p<；0.05)无声音的最佳估计，受试者特定的平均负荷(MAE=0.31±0.12BW)。这表明，联合声发射包含与内部联合接触力相关的信息，并且该信息是一致的，因此可以估计独特的情况。
Quantifying joint load in activities of daily life could lead to improvements in mobility for numerous people; however, current methods for assessing joint load are unsuitable for ubiquitous settings. The aim of this study is to demonstrate that joint acoustic emissions contain information to estimate this internal joint load in a potentially wearable implementation. Eleven healthy, able-bodied individuals performed ambulation tasks under varying speed, incline, and loading conditions while joint acoustic emissions and essential gait measures—electromyography, ground reaction forces, and motion capture trajectories—were collected. The gait measures were synthesized using a neuromuscular model to estimate internal joint contact force which was the target variable for subject-specific machine learning models (XGBoost) trained based on spectral, temporal, cepstral, and amplitude-based features of the joint acoustic emissions. The model using joint acoustic emissions significantly outperformed (p< 0.05) the best estimate without the sounds, the subject-specific average load (MAE = 0.31 ± 0.12 BW), for both seen (MAE = 0.08 ± 0.01 BW) and unseen (MAE = 0.21 ± 0.05 BW) conditions. This demonstrates that joint acoustic emissions contain information that correlates to internal joint contact force and that information is consistent such that unique cases can be estimated.