Estimating Knee Joint Load Using Acoustic Emissions During Ambulation
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Annals of Biomedical Engineering (Ó 2020) https://doi.org/10.1007/s10439-020-02641-7
Original Article
Estimating Knee Joint Load Using Acoustic Emissions During Ambulation KEATON L. SCHERPEREEL ,1 NICHOLAS B. BOLUS,2 HYEON KI JEONG,2 OMER T. INAN,2 and AARON J. YOUNG1 1
Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA; and 2School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA (Received 9 June 2020; accepted 26 September 2020) Associate Editor Michael R. Torry oversaw the review of this article.
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. Keywords—Tibiofemoral contact force, Joint sounds, Machine learning, Knee joint load.
INTRODUCTION Knee joint load—specifically, tibiofemoral contact force—is an important metric for determining the progression of diseases,2,12 predicting injury risk,16,41
Address correspondence to Keaton L. Scherpereel, Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. Electronic mail: [email protected]
evaluating rehabilitation approaches,49 and designing implants.44 Quantifying joint load in activities of daily life can inform strategies to improve mobility and quality of life for people across ages and activity levels. Knee joint load is measured using knee joint contact force which is defined as the force experienced at locations of contact between the tibial plateau and the femoral condyles. This force is comprised of forces externally applied through the knee via ground contact and muscle actions surrounding the knee. Currently, there are no non-invasive methods for directly measuring internal joint load: joint cont
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