Deep Learning based Motion Prediction for Exoskeleton Robot Control in Upper Limb Rehabilitation

Abstract

The synchronization of the movement between exoskeleton robot and human arm is crucial for Robot-assisted training (RAT) in upper limb rehabilitation. In this paper, we propose a deep learning based motion prediction model which is applied to our recently developed 8 degrees-of-freedom (DoFs) upper limb rehabilitation exoskeleton, named NTUH-II. The human arm dynamics and surface electromyography (sEMG) can be first measured by two wireless sensors and used as input of deep learning model to predict user's motion. Then, the prediction can be used as desired motion trajectory of the exoskeleton. As a result, the robot arm can follow the movement on either side of the user's arm in real-time. Various experiments have been conducted to verify the performance of the proposed motion prediction model, and the results show that the proposed motion prediction implementation can reduce the mean absolute error and the average delay time of movement between human arm and robot arm.