Classification of Motor Control Difficulty using EMG in Physical Human-Robot Interaction

In physical human-robot interaction, a variable admittance/impedance controller is desired to adjust its controller parameters to enhance the collaboration by minimizing the human effort and maximizing the stability. In this paper, we propose a physiological monitoring approach based on electroencephalogram activities to classify the motor control difficulty and use that information for adjusting an admittance controller. We designed a physical human-robot interaction experiment where the human guides the robot's end-effector across four tasks with varying motor control difficulty. Each task is a combination of high/low damping and fine/gross motor control. During the experiments, we measure the muscle activation information in terms of surface electromyogram from eight channels. Two sets of features based on Riemann geometry and time domain (Hudgins' features) are extracted every 500 ms from the EMG data. A support vector machine classifier is trained on these features to estimate whether the existing admittance parameters are comfortable for the user else an increase/decrease of the damping is suggested. Riemann geometry-based features yielded higher accuracy (85.7%) than the Hudgins' features (69.1%) across 21 participants; however, the performance of these classifiers on the new sessions degraded to 63.1% and 54.5% respectively. To address this issue, we implemented a transfer learning approach using Riemannian features that improved the inter-session detection rate to 73.95%.