Estimation of continuous and constraint-free 3 DoF wrist movements from surface electromyogram signal using kernel recursive least square tracker

We have employed Kernel Least Square Tracker (KRLS-T), a nonlinear kernel based recursive algorithm, to estimate 3 dimensional wrist kinematics from sEMG signals of forearm muscle groups. KRLS-T combines the advantage of kernel techniques and adaptive estimation and hence has been considered for predicting 3 dimensional wrist angles from nonlinear and non-stationary sEMG. We have been able to successfully predict 6 basic and 2 dynamic, continuous and constraint-free wrist motions for 10 normal subjects in an offline mode with more than 90% accuracy. The continuous wrist motion profiles, considered here, resemble the complex and dexterous wrist motions involved in various activities of daily life. Statistical significance analysis shows that KRLS-T performs better than Kernel Ridge Regression (KRR) and a feed-forward back propagation neural network during a 10-fold cross validation stage. Subsequently, a real-life scenario has been emulated for the KRLS-T based motion predictor where 2 different trials' data are combined and given sequentially as input to the estimator. Its fast adaptation capability to the nonstationary sEMG-wrist angle relationship, as reported here, makes it a promising option for implementing intuitive prosthesis control.