E2CNN: An Efficient Concatenated CNN for Classification of Surface EMG Extracted From Upper Limb-Reference-Cited by-同舟云学术

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E2CNN: An Efficient Concatenated CNN for Classification of Surface EMG Extracted From Upper Limb

Published:2023-04-15 Issue:8 Volume:23 Page:8989-8996
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Qureshi Muhammad Farrukh¹^ORCID,Mushtaq Zohaib¹,Rehman Muhammad Zia Ur²,Kamavuako Ernest Nlandu³^ORCID

Affiliation:

1. Department of Electrical Engineering, Riphah International University, Islamabad, Pakistan

2. Department of Biomedical Engineering, Riphah International University, Islamabad, Pakistan

3. Department of Engineering, Centre for Robotics Research, King’s College London, London, U.K.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/7361/10102602/10071966.pdf?arnumber=10071966

Reference34 articles.

1. Surface-Electromyography-Based Gesture Recognition by Multi-View Deep Learning

2. Deep Learning with Convolutional Neural Networks Applied to Electromyography Data: A Resource for the Classification of Movements for Prosthetic Hands

3. Research on sEMG-Based Gesture Recognition by Dual-View Deep Learning

4. Stacked Sparse Autoencoders for EMG-Based Classification of Hand Motions: A Comparative Multi Day Analyses between Surface and Intramuscular EMG

5. Research on Gesture Recognition of Surface EMG Based on Machine Learning

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