This paper proposes a novel control method of using the surface electromyogram (sEMG) signals to predict the kinematics of hand and wrist, which will be applied in the prosthetic hand control. Prediction of movement in 3 degree-of-freedoms’ (DoFs’) (wrist flexion/extension (WFE), lateral abduction/adduction (LAA), and hand open/close (HOC)) is investigated in this paper. The proposed control method contains a time-delay recurrent neural network (TDRNN), adopting the previous prediction of the joint angles and the time-delay sEMG signals as the system input. This proposed method uses a batch training based on Levenberg–Marquardt (LM) algorithm to learn the weights of the TDRNN. The trained TDRNN is aimed to achieve simultaneous and proportional regression from human movements of the 3 DoFs to those of the prosthetic hand. Three able-bodied subjects are chosen to participate in the test and demonstrate its feasibility and performance. The offline test result R2 ranges between 0.81 and 0.94. The online test results show that TDRNN reacts faster, which verifies that the method proposed in this paper will be feasible and effective in prosthetic hand control.
