Replication of Impedance Identification Experiments on a
  Reinforcement-Learning-Controlled Digital Twin of Human Elbows
Hao Yu Zebin Huang Qingbo Liu Ignacio Carlucho Mustafa Suphi Erden
This study presents a pioneering effort to replicate human neuromechanical
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 elbow were replicated on a musculoskeletal
model. We compared the elbow movement controlled by an RL agent with the motion
of an actual human elbow in terms of the impedance identified in
torque-perturbation experiments. The findings reveal that the RL agent exhibits
higher elbow impedance to stabilise the target elbow motion under perturbation
than a human does, likely due to its shorter reaction time and superior sensory
capabilities. This study serves as a preliminary exploration into the potential
of virtual environment simulations for neuromechanical research, offering an
initial yet promising alternative to conventional experimental approaches. An
RL-controlled digital twin with complete musculoskeletal models of the human
body is expected to be useful in designing experiments and validating
rehabilitation theory before experiments on real human subjects.