Design of exoskeleton brain-like intelligent trajectory prediction model based on echo state network

Traditional rehabilitation training methods face significant challenges, such as low repeatability and a shortage of skilled physicians. Exoskeleton robots have been recognized by rehabilitation experts as valuable tools in addressing these issues. However, current auxiliary training devices suffer from limited human-computer interaction capabilities, single-mode training, and basic passive functionalities. To enhance the effectiveness of rehabilitation training, particularly in predicting human movement trajectories, this study presents a brain-like intelligent trajectory prediction model. This model, inspired by bionics, follows the physiological structure and control mechanisms of the human brain to improve human-robot cooperative control in rehabilitation exoskeletons. Utilizing an Echo State Network (ESN), the model establishes a computational framework that mirrors the motor neuron activity of the cerebellum, brainstem, and spinal cord. In conjunction with the Spiking Cerebellar Model Network (SCMN), a brain-like trajectory prediction model was developed that incorporates pulsatile neurons, simulating the transmission and synaptic processes observed in biological neural networks. This approach enhances computational efficiency and physiological interpretability, addressing the limitations of existing neural network models. Experimental results demonstrate that the proposed brain-like control model effectively predicts the movement trajectories of upper limb rehabilitation exoskeletons, offering a novel theoretical and practical framework for bionic control in rehabilitation robotics.