EMG Based Simultaneous Wrist Motion Prediction Using Reinforcement Learning

Abstract

Advanced robotic devices have the potential to improve both clinical and home-based rehabilitation procedures in stroke therapy. Having an active, intelligent device that can interact with the patient in both actuation and sensing feedback from the body would help improve the assessment of rehabilitation. Reliable signal detection and recognition of user intents are the key points of developing active robotic devices. Surface Electromyography (sEMG) technique is commonly used for non-invasive biological signal detection from muscle activations. This work presents a simple Convolutional Neural Network (CNN) model combined with A2C actor-critic algorithm-based reinforcement learning to predict simultaneous wrist motion intention direction. The proposed model was tested with experimental 2-channel sEMG datasets using both deep features extracted from CNN and hand-crafted features. We achieved an average accuracy of approximately 92% regardless of the instantaneous angular position of the wrist. We also presented generalization test results to demonstrate the performance of the model to a completely new subject's sEMG data.