Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer-Reference-Cited by-同舟云学术

Boosting Personalised Musculoskeletal Modelling with Physics-informed Knowledge Transfer

Published:2022 Issue: Volume: Page:1-1
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Zhang Jie¹^ORCID,Zhao Yihui¹^ORCID,Bao Tianzhe²^ORCID,Li Zhenhong¹^ORCID,Qian Kun¹^ORCID,Frangi Alejandro F.³^ORCID,Xie Sheng Quan¹^ORCID,Zhang Zhi-Qiang¹^ORCID

Affiliation:

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

2. School of Rehabilitation Sciences and Engineering, University of Health and Rehabilitation Sciences, Qingdao, China

3. School of Computing, University of Leeds, Leeds, U.K.

Funder

U.K. EPSRC

EU Marie Curie Individual Fellowship

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/4407674/09975275.pdf?arnumber=9975275

Reference40 articles.

1. Decoding Muscle Force From Motor Unit Firings Using Encoder-Decoder Networks

2. EEGWaveNet: Multi-Scale CNN-Based Spatiotemporal Feature Extraction for EEG Seizure Detection

3. Toward Robust, Adaptiveand Reliable Upper-Limb Motion Estimation Using Machine Learning and Deep Learning–A Survey in Myoelectric Control

4. MetaSleepLearner: A Pilot Study on Fast Adaptation of Bio-Signals-Based Sleep Stage Classifier to New Individual Subject Using Meta-Learning

5. Machine learning for rapid estimation of lower extremity muscle and joint loading during activities of daily living

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