Mirror motion recognition method about upper limb rehabilitation robot based on sEMG

Abstract

A novel method of mirror motion recognition by rehabilitation robot with multi-channels sEMG signals is proposed, aiming to help the stroked patients to complete rehabilitation training movement. Firstly the bilateral mirror training is used and the model of muscle synergy with basic sEMG signals is established. Secondly, the constrained L1/2-NMF is used to extracted the main sEMG signals information which can also reduce the limb movement characteristics. Finally the relationship between sEMG signal characteristics and upper limb movement is described by TSSVD-ELM and it is applied to improve the model stability. The validity and feasibility of the proposed strategy are verified by the experiments in this paper, and the rehabilitation robot can move with the mirror upper limb. By comparing the method proposed in this paper with PCA and full-action feature extraction, it is confirmed that convergence speed is faster; the feature extraction accuracy is higher which can be used in rehabilitation robot systems.