Estimating Biomechanical Risk Factors of Knee Osteoarthritis in Gait Using Instrumented Shoe Insole and Deep Learning Approaches

Abstract

This study aims to implement an alternative to the cost-ineffective and time consuming current inverse dynamics approaches and predict knee adduction moments, a known predictor of knee osteoarthritis, through deep learning neural networks and a custom instrumented insole. Feed-forward, convolutional, and recurrent neural networks are applied to the data extracted from five piezo-resistive force sensors attached to the insole of a shoe. All models predicted knee adduction moment variables during walking with high correlation coefficients, greater than 0.72, and low root mean squared errors, ranging from 0.6-1.2%. The convolutional neural network is the most accurate predictor followed by the recurrent and feed-forward neural networks. These findings and the methods presented in the current study are expected to facilitate a cost-effective clinical analysis of knee adduction moments and to simplify future research studying the relationship between knee adduction moments and knee osteoarthritis.