A Statistical Learning Approach for Cable-driven Robot in Rehabilitation Training

Cable-driven rehabilitation robots can be used to recover the upper-limb motor function of hemiplegia patients after stroke. In this study, a statistical approach was proposed to enable a 3-DOF upper-limb cable-driven rehabilitation robot (UCDRR) to predict the human intention by learning human skills from demonstrations. Specifically, in the learning phase, Gaussian mixture model (GMM) was exploited to statistically analyze the human-robot interaction features (i.e., Electromyography (EMG) and interaction force) based on the recorded demonstrator-robot interaction data. Then, during prediction, the robot estimated the interaction force from the measured EMG by using Gaussian mixture regression (GMR). The experimental results of the interaction force prediction in simulation demonstrated that the proposed approach could statistically extract the variable information from the multiple demonstrations successfully and estimate subject-robot interaction forces with high accuracy. This study will help the UCDRR to provide assistance as needed to hemiplegia patients during rehabilitation training, thereby promoting the active participation.