Rehabilitation exercise based on brain-machine interaction with wearable robot

This study proposes deep learning called Convolutional Neural Network (CNN), which can enhance the classification performance of five different rehabilitation exercise scenarios based on brain-machine interaction (BMI). Also, it further presents the feasibility of helping stroke patients and healthy people to assist in rehabilitation exercise. This work designs a wearable hand robot and five different motor images (MI) for exercise guidance on the computer screen. A participant is also asked to set on a chair to acquire the cerebral response signals using the function near-infrared spectroscopy (fNIRS). Deep learning called convolutional neural network (CNN) is utilized to extract and classify the collected data and make commands to the wearable hand robot. The classification accuracy of the S_2 MI is the highest value for participant 1; the classification accuracy of the S_1 MI is the highest value for participant 2. Besides, the S_5 and S_3 MI showed the lowest classification accuracy for participant one and participant two, respectively. Propose a CNN framework with visual guidance to control wearable robots to reduce incorrect commands, and present a rehabilitation exercise feasibility to stroke patients and healthy people with less time.