Multi-Attention Feature Fusion Network for Accurate Estimation of Finger Kinematics From Surface Electromyographic Signals-Reference-Cited by-同舟云学术

Multi-Attention Feature Fusion Network for Accurate Estimation of Finger Kinematics From Surface Electromyographic Signals

Published:2023-06 Issue:3 Volume:53 Page:512-519
ISSN:2168-2291
Container-title:IEEE Transactions on Human-Machine Systems
language:
Short-container-title:IEEE Trans. Human-Mach. Syst.

Author:

Guo Weiyu¹^ORCID,Jiang Ning²^ORCID,Farina Dario³^ORCID,Su Jingyong⁴^ORCID,Wang Zheng⁵^ORCID,Lin Chuang⁶^ORCID,Xiong Hui¹^ORCID

Affiliation:

1. Artificial Intelligence Thrust, Information Hub, The Hong Kong University of Science and Technology, Guangzhou, China

2. National Clinical Research Center for Geriatrics, Sichuan University West China Hospital; Med-X Center for Manufacturing, Sichuan University, Sichuan, China

3. Department of Bioengineering, Imperial College London, London, U.K.

4. School of Computer Science, Harbin Industry University, Heilongjiang, China

5. Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Beijing, China

6. School of Information Science and Technology, Dalian Maritime University, Dalian, China

Funder

Foshan HKUST Projects

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Science Applications,Human-Computer Interaction,Signal Processing,Control and Systems Engineering,Human Factors and Ergonomics

Link

http://xplorestaging.ieee.org/ielx7/6221037/10146026/10086669.pdf?arnumber=10086669

Reference42 articles.

1. Gaussian Process Autoregression for Simultaneous Proportional Multi-Modal Prosthetic Control With Natural Hand Kinematics

2. sEMG-Based Continuous Estimation of Finger Kinematics via Large-Scale Temporal Convolutional Network

3. Temporal Variability Analysis in sEMG Hand Grasp Recognition using Temporal Convolutional Networks

4. Prediction of joint moments using a neural network model of muscle activations from EMG signals

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