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.

量化日常生活活动中的关节负荷可以改善许多人的行动能力；然而，当前用于评估关节负荷的方法不适用于无处不在的环境。本研究的目的是证明关节声发射包含用于估计潜在可穿戴实施中的这种内部关节负载的信息。11 名身体健全的健康人在不同的速度、倾斜度和负载条件下执行步行任务，同时收集了关节声发射和基本步态测量（肌电图、地面反作用力和动作捕捉轨迹）。步态测量是使用神经肌肉模型合成的，以估计内部关节接触力，这是基于光谱训练的特定主题机器学习模型 (XGBoost) 的目标变量，联合声发射的时间、倒谱和基于幅度的特征。使用联合声发射的模型明显优于（p < 0.05) 没有声音的最佳估计，特定主题的平均负载 (MAE = 0.31 ± 0.12 BW)，对于可见 (MAE = 0.08 ± 0.01 BW) 和看不见 (MAE = 0.21 ± 0.05 BW) 条件。这表明关节声发射包含与内部关节接触力相关的信息，并且该信息是一致的，因此可以估计独特的情况。