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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 based on 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.
Original languageEnglish
Title of host publicationIEEE World Congress on Computational Intelligence (IEEE WCCI 2024)
PublisherIEEE
Publication statusAccepted/In press - 15 Mar 2024
EventInternational Joint Conference on Neural Networks (IJCNN 2024)
under IEEE World Congress on Computational Intelligence (IEEE WCCI 2024)
- Yokohama, Yokohama, Japan
Duration: 30 Jun 20245 Jul 2024
https://2024.ieeewcci.org/

Conference

ConferenceInternational Joint Conference on Neural Networks (IJCNN 2024)
under IEEE World Congress on Computational Intelligence (IEEE WCCI 2024)
Country/TerritoryJapan
CityYokohama
Period30/06/245/07/24
Internet address

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