Objective: Considering the knee as a fluid-lubricated system, articulating surfaces undergo different lubrication modes and generate joint acoustic emissions (JAEs). The goal of this study is to compare knee biomechanical signals against synchronously recorded joint sounds and assess the hypothesis that JAEs are attributed to tribological origins.
Methods: JAE, electromyography, ground reaction force signals, and motion capture markers were synchronously recorded from ten healthy subjects while performing two-leg and one-leg squat exercises. The biomechanical signals were processed to calculate a tribological parameter, lubrication coefficient, and JAEs were divided into short windows and processed to extract 64-time-frequency features. The lubrication coefficients and JAE features of two-leg squats were used to label the windows and train a classifier that discriminates the knee lubrication modes only based on JAE features.
Results: The classifier was used to predict the label of one-leg squat JAE windows and it achieved a high test-accuracy of 84%. The Pearson correlation coefficient between the estimated friction coefficient and predicted JAE scores was 0.83 ± 0.08. Furthermore, the lubrication coefficient threshold, separating two lubrication modes, decreased by half from two-leg to one-leg squats. This result was consistent with tribological changes in the knee load as it was inversely doubled in one-leg squats.
Significance: This study supports the potential use of JAEs as a quantitative biomarker to extract tribological information. Since arthritis and similar disease impact the roughness of the joint cartilage, the use of JAEs could have broad implications for studying joint frictions and monitoring joint structural changes with wearable devices.