Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer

Zhang, J, Zhao, Y, Bao, T et al. (5 more authors) (2022) Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer. IEEE Transactions on Instrumentation and Measurement, 72. 2500811. ISSN 0018-9456

Abstract

Metadata

Authors/Creators:
Copyright, Publisher and Additional Information: This is an author produced version of an article accepted for publication in IEEE Transactions on Instrumentation and Measurement, made available under the terms of the Creative Commons Attribution License (CC-BY), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.
Keywords: Personalized musculoskeletal model; physics-informed deep transfer learning; surface electromyogram (sEMG); wrist muscle forces and joint kinematics estimation
Dates:
  • Accepted: 14 November 2022
  • Published (online): 8 December 2022
  • Published: 8 December 2022
Institution: The University of Leeds
Academic Units: The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Computing (Leeds)
The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Electronic & Electrical Engineering (Leeds) > Robotics, Autonomous Systems & Sensing (Leeds)
Funding Information:
FunderGrant number
EPSRC (Engineering and Physical Sciences Research Council)EP/S019219/1
EU - European Union101023097
Depositing User: Symplectic Publications
Date Deposited: 22 Nov 2022 11:50
Last Modified: 28 Jan 2023 01:16
Status: Published
Publisher: IEEE
Identification Number: https://doi.org/10.1109/TIM.2022.3227604

Export

Statistics