Continuous Estimation of Knee Joint Angle during Squat from sEMG using Artificial Neural Networks

Abstract

The purpose of this research was to continuous knee joint angle estimation from sEMG during squat using artificial neural networks. sEMG signals of vastus medialis, rectus femoris, biceps femoris and 3D kinematics of lower extremity joints for four participants during squat were captured at 1500 Hz and 100 Hz, respectively. sEMG signals were preprocessed and RMS and variance were extracted as input features. The processed input data was given to a three-layer feed forward neural network with one hidden layer. The proposed network was trained by the Levenberg-Marquardt algorithm. The root mean square error (RMSE) and correlation coefficient (CC) were used to evaluate the accuracy of estimation. The results showed that this network is able to continuously estimate the knee joint angle with global RMSE of 5.0041° ± 0.9963° and CC of 0.9898 ± 0.0039. It concludes that a multilayer neural network with a simple structure has the ability to continuously estimate the joint angle from sEMG data while performing an athletic movement under real loading situation.