Research on hand rehabilitation exoskeleton based on sEMG signal predictive control method

Abstract

At present, the hand rehabilitation training system mainly adopts passive rehabilitation training method, and the training mode is relatively simple, which cannot reflect the movement intention of patients. This paper has designed and produced a kind of predictive control based on the methods of electricity hand rehabilitation training system, the system can according to your hand on the multi-channel sEMG predict intentions and movement angle, and then drive the exoskeleton robot assisted hand movement, as reflected in training patients' movement intentions, to realize active rehabilitation training. In order to achieve the compliance of the control and prevent the secondary injury to patients, this paper designed the exoskeleton manipulator sliding mode control method. The simulation results and experimental results verify the correctness of the design. The sEMG acquisition and prediction system can accurately predict the motion intention of patients, and the steady-state error of the final control can be kept within 5 degrees, with good accuracy and reliability, which is expected to be applied in the hand rehabilitation training of stroke patients.