Quantifying Asymmetry between Medial and Lateral Compartment Knee Loading Forces using Acoustic Emissions.

Objective Osteoarthritis is the most common type of knee arthritis that can be affected by excessive and compressive loads and can affect one or more compartments of the knee: medial, lateral, and patellofemoral. The medial compartment tends to be the most vulnerable to injuries and research suggests that a better understanding of the medial to lateral load distribution conditions could provide insights to the quantitative usage of knee compartments in activities of daily life. Methods To that end, we present a novel method to quantify the directional bias of asymmetry between the medial and lateral compartment knee joint load by recording knee acoustical emissions and analyzing them using a deep neural network in a subject independent model. We placed four miniature contact microphones on the medial and lateral sides of the patella on both the left and right leg. We compared the handcrafted audio features with the automated features extracted from the convolutional autoencoder which is an unsupervised model that learns the comprehensive representation of the input to determine whether these automated features can better represent the signals characteristic in regard to the structural asymmetry of the knee joint. The input to the convolutional auto encoder (CAE) is a time-frequency representation and different types of these images such as spectrogram and scalogram are compared. We also compared the multi-sensor fusion approach with the performance of a single sensor to determine the robustness of using multiple sensors. Results Using a representation learning based approach, we developed a subject independent classification model capable of classifying the asymmetry of the medial and lateral joint load across subjects (accuracy = 83%). Conclusion The result indicates that wavelet coherence which is the time-frequency correlation of two signals using a wavelet transform yields the best accuracy. Significance These findings suggest that acoustic signals could potentially quantify the direction of medial to lateral load distribution which would broaden the implications for wearable sensing technology for monitoring cartilage health and factors responsible for cartilage breakdown and assessing appropriate rehabilitation exercises without overloading on one side.