Physics-Informed Deep Learning for Muscle Force Prediction With Unlabeled sEMG Signals-Reference-Cited by-同舟云学术

Physics-Informed Deep Learning for Muscle Force Prediction With Unlabeled sEMG Signals

Published:2024 Issue: Volume:32 Page:1246-1256
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Ma Shuhao¹^ORCID,Zhang Jie²^ORCID,Shi Chaoyang³^ORCID,Di Pei⁴,Robertson Ian D.¹^ORCID,Zhang Zhi-Qiang¹^ORCID

Affiliation:

1. School of Electronic and Electrical Engineering, University of Leeds, Leeds, U.K

2. Centre for Wireless Innovation, Queen’s University Belfast, Belfast, U.K

3. Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, School of Mechanical Engineering, Tianjin University, Tianjin, China

4. Rex Bionics Ltd., Auckland, New Zealand

Funder

United Kingdom Research and Innovation (UKRI) Horizon Europe Guarantee

Royal Society

European Union (EU) Marie Curie Individual Fellowship

China Scholarship Council

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7333/10398877/10464365.pdf?arnumber=10464365

Reference43 articles.

1. Adaptive force regulation of muscle strengthening rehabilitation device with magnetorheological fluids

2. Pinpointed muscle force control in consideration Of human motion and external force

3. Robust walking control of a lower limb rehabilitation exoskeleton coupled with a musculoskeletal model via deep reinforcement learning

4. Robust Ground Reaction Force Estimation and Control of Lower-Limb Prostheses: Theory and Simulation

5. Surface electromyography based muscle fatigue detection using high-resolution time-frequency methods and machine learning algorithms

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