Abstract
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.
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This material is based upon work supported in part by the National Science Foundation (NSF) / National Institutes of Health Smart and Connected Health Program under Grant No. 1R01EB023808 and the NSF NRT Traineeship Program Grant No. 1545287.
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Scherpereel, K.L., Bolus, N.B., Jeong, H.K. et al. Estimating Knee Joint Load Using Acoustic Emissions During Ambulation. Ann Biomed Eng 49, 1000–1011 (2021). https://doi.org/10.1007/s10439-020-02641-7
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DOI: https://doi.org/10.1007/s10439-020-02641-7