Control design of upper limb rehabilitation exoskeleton robot based on long and short-term memory network

Abstract

Abstract The increasing human resources in rehabilitation training for patients with upper extremity impairment, more convenient and objective in rehabilitation training. Therefore, an upper limb rehabilitation exoskeleton robot controller based on a long-short-term memory network (LSTM), combined with impedance control is proposed. The design adopts the LSTM deep learning network to realize the preprocessing and classification of the collected patient’s subtle intention signals to obtain the patient’s real movement intention. The impedance controller based on the compliant control method is designed, and completed the auxiliary exercise for the patient. In the whole process, the LSTM algorithm effectively handles the timing problem, that is, effectively and continuously processes the patient’s data. The impedance control provides a good buffer for assisting the patient’s movement, ensuring the good physical compatibility of the patient-robot system with the outside world. Finally, through the model data test, the recognition and differentiation of 18 human body movements were tested, and the accuracy reached 59.3%.