Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG

Zhang, J, Zhao, Y, Shone, F orcid.org/0000-0003-4602-8861 et al. (4 more authors) (2023) Physics-informed Deep Learning for Musculoskeletal Modelling: Predicting Muscle Forces and Joint Kinematics from Surface EMG. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31. 484 -493. ISSN 1534-4320

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Item Type: Article
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This item is protected by copyright. This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited.

Keywords: Musculoskeletal modelling; deep neural network; physics-based domain knowledge; muscle forces and joint kinematics prediction
Dates:
  • Published: 1 February 2023
  • Published (online): 5 December 2022
  • Accepted: 27 November 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:
Funder
Grant number
EU - European Union
101023097
EPSRC (Engineering and Physical Sciences Research Council)
EP/S019219/1
Depositing User: Symplectic Publications
Date Deposited: 06 Dec 2022 14:42
Last Modified: 23 May 2024 15:12
Status: Published
Publisher: Institute of Electrical and Electronics Engineers
Identification Number: 10.1109/TNSRE.2022.3226860
Open Archives Initiative ID (OAI ID):

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