A Real-time sEMG-based Control Strategy and System for Contralaterally Controlled Functional Electrical Stimulation

Function electrical stimulation (FES) based on surface electromyography (sEMG) has been realized an effective rehabilitation method, but it still can’t control continuously well. This study proposes an sEMG-based control strategy for contralaterally controlled functional electrical stimulation (CCFES). Compared with other strategies, the sEMG-based control strategy can be better used during grasping objects and bearing weights. This algorithm divides the function into three segments according to the sEMG under different forces, and calculates the current value of FES in proportion to the root mean square (RMS) of sEMG in each segment. Moreover, a CCFES system combined with select support vector machine (SVM) and this algorithm is designed to verify the feasibility of the strategy. Six healthy right-handed subjects were recruited to take part in the experiment. The correlation coefficients (r) of the joint angles of the two wrists is used to evaluate the accuracy of the algorithm. Three out of six subjects closely matched the voluntary movement (r>0.9) and the correlation coefficients of all subjects is greater than 0.8. These results demonstrated the feasibility of this sEMG-based control strategy.