Research on rehabilitation training bed with action prediction based on NARX neural network

Abstract

Rehabilitation training can effectively help the elderly recover their self‐care state and enhance physical fitness. As surface electromyography analysis is effective to recognize motion intention, researchers use it to develop prosthetic limb operations. In this article, the rehabilitation training bed is designed by combining the rehabilitation training with the motion prediction based on the surface myoelectric signal, which can recognize the tilt of the upper body in different directions and provide corresponding assistance to the elderly. After collecting EMG signal, the effective signal was dimensional reduction, mapped by linear discriminant analysis. To train and recognize the EMG‐motion mapping relationship, we used a recurrent neural network called nonlinear autoregressive with exogenous input model and used a 360° tilt prediction experiment on the upper body. Results showed that the root mean squared error and the error autocorrelation coefficient were relatively low, and the tilt degree of the experimenter was highly matched.

Gaussian Process Autoregression for Joint Angle Prediction Based on sEMG Signals

“…Gupta proposed an ankle joint angle estimation model based on the NARX model using sEMG signals and knee joint angle signals, and the performance of the model proved its applicability to ankle joint angle estimation for active prosthesis, orthosis and lower limb rehabilitation (26). Liu et al used (27). Raj et al proposed a multilayer perceptron neural network model based on the NARX model for estimating elbow joint angle and elbow joint angular velocity, and the proposed model estimated elbow joint angle and elbow joint angular velocity with high accuracy (28).…”