In The Knee ; h5-index 38.0
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.
Snyder Samantha J, Chu Edward, Um Jumyung, Heo Yun Jung, Miller Ross H, Shim Jae Kun
2023-Jan-17
Knee adduction moment, Knee osteoarthritis, Machine learning, Neural network
