A linear-attention based network for estimating continuous upper limb movement from surface electromyography.

Continuous kinematics estimation methods are important in human-machine interaction systems because they offer a more natural and intuitive result than pattern-recognition methods. The estimation of upper-limb movement is important, which involves the estimation of the movement of the elbow and shoulder joints. However, the deformations of muscles around the elbow and shoulder can cause the shifting of the angle sensors. Nevertheless, Vicon can measure joint angles using cameras that have no contact with muscles. In this paper, we propose a Linear-Attention-based model (LABD) rather than other deep learning models to estimate upper-limb movements. The sEMG signals were collected from eight sEMG sensors and angles were measured from Vicon. The experimental results of LABD were compared with multilayer perceptrons (MLP), Temporal Convolutional Network (TCN), long short-term memory network (LSTM), and a dot-product attention-based model (DABD). The Pearson correlation coefficients (PCC) between the target and estimated joint angle sequences were calculated to evaluate the performance of each model. The Wilcoxon signed-rank results showed that the LABD significantly outperformed the other models.