Computationally Efficient Personalized EMG-Driven Musculoskeletal Model of Wrist Joint

Author:

Zhao Yihui1ORCID,Zhang Jie1ORCID,Li Zhenhong1ORCID,Qian Kun1ORCID,Xie Sheng Quan1ORCID,Lu Yu2ORCID,Zhang Zhi-Qiang1ORCID

Affiliation:

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

2. Faculty of Education, School of Educational Technology, Beijing Normal University, Beijing, China

Funder

U.K. Engineering and Physical Sciences Research Council

European Union (EU) Marie Curie Individual Fellowship

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Cited by 8 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Digital Sensing Systems for Electromyography;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2024

2. Physics-Informed Deep Learning for Muscle Force Prediction With Unlabeled sEMG Signals;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2024

3. A Computational Method to Identify Optimal Functional Muscle Synergies From Estimated Muscle Activations;IEEE Transactions on Instrumentation and Measurement;2024

4. Consistent control information driven musculoskeletal model for multiday myoelectric control;Journal of Neural Engineering;2023-09-15

5. Physics-Informed Deep Learning for Musculoskeletal Modeling: Predicting Muscle Forces and Joint Kinematics From Surface EMG;IEEE Transactions on Neural Systems and Rehabilitation Engineering;2023

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