SEMG-based multifeatures and predictive model for knee-joint-angle estimation

Abstract

Surface electromyography (sEMG) signals are commonly used in activity monitoring and rehabilitation training as they reflect effectively the motor intentions of users. This study proposed a new sEMG-based multifeature extraction and predictive model to predict knee-joint angle from multichannel sEMG. Six channels of sEMG from relevant muscles were recorded, and knee-joint angles were sampled simultaneously for six kinds of knee-joint movement models. The root-mean-square (RMS), wavelet coefficients (WC), and permutation entropy (PE) as features of sEMG were extracted. The back propagation neural network, generalized regression neural network, and least-square support vector regression machine (LS-SVR) were used as predictive models. To validate the effectiveness of the sEMG features and predictive models, twelve subjects without neural or musculoskeletal deficits participated in the experiment. Six kinds of knee-joint movement models at different speeds and different loads were respectively conducted by the subjects. Results revealed that the combination of the three features (RMS, WC, and PE) and LS-SVR performed well for the knee-joint-angle of all kinds of leg motions. The RMS error for all kinds of leg motions was <7.7°. The estimation results of joint motion state would be used to rehabilitation robot or functional electrical stimulation for active rehabilitation of spinal cord injury patients or stroke patients.