Prediction of knee adduction moment using innovative instrumented insole and deep learning neural networks in healthy female individuals

Background: The knee adduction moment, a biomechanical risk factor of knee osteoarthritis, is typically measured in a gait laboratory with expensive equipment and inverse dynamics modeling software. We aimed to develop a framework for a portable knee adduction moment estimation for healthy female individuals using deep learning neural networks and custom instrumented insole and evaluated its accuracy compared to the standard inverse dynamics approach. Methods: Feed-forward, convolutional, and recurrent neural networks were applied to the data extracted from five piezo-resistive force sensors attached to the insole of a shoe. Results: All models predicted knee adduction moment variables during walking with high correlation coefficients, r > 0.72, and low root mean squared errors (RMSE), ranging from 0.5% to 1.2%. The convolutional neural network is the most accurate predictor of average knee adduction moment (r = 0.96; RMSE = 0.5%) followed by the recurrent and feed-forward neural networks. Conclusion: These findings and the methods presented in the current study are expected to facilitate a cost-effective clinical analysis of knee adduction moment for healthy female individuals and to facilitate future research on prediction of other biomechanical risk factors using similar methods.