Replication of Impedance Identification Experiments on a Reinforcement-Learning-Controlled Digital Twin of Human Elbows

This study presents a pioneering effort to replicate human neuromechanics experiments within a virtual environment utilising a digital human model. By employing MyoSuite, a state-of-the-art human motion simulation platform enhanced by Reinforcement Learning (RL), multiple types of impedance identification experiments of human elbows were replicated on a digital musculoskeletal model. We compared the motor control capability of an RL agent with that of an actual human elbow in terms of the impedance identified through torque perturbation. The findings reveal that the RL agent exhibits higher elbow impedance to stabilise the target elbow motion under perturbation than a human does. It is likely due to the shorter reaction time and superior sensory capabilities of the RL agent. This study serves as a preliminary exploration into the potential of human digital twins for neuromechanics experiments. An RL-controlled digital twin with the musculoskeletal structure of the human body is expected to be useful in validating rehabilitation techniques before experiments on real human subjects.