Electromyography Based Gesture Decoding Employing Few-Shot Learning, Transfer Learning, and Training From Scratch-Reference-Cited by-同舟云学术

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Electromyography Based Gesture Decoding Employing Few-Shot Learning, Transfer Learning, and Training From Scratch

Published:2023 Issue: Volume:11 Page:104142-104154
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Godoy Ricardo V.¹^ORCID,Guan Bonnie¹,Sanches Felipe¹,Dwivedi Anany²^ORCID,Liarokapis Minas¹^ORCID

Affiliation:

1. Department of Mechanical and Mechatronics Engineering, New Dexterity Research Group, The University of Auckland, Auckland, New Zealand

2. Artificial Intelligence (AI) Institute, Division of Health, Engineering, Computing and Science, University of Waikato, Hamilton, New Zealand

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10258264.pdf?arnumber=10258264

Reference48 articles.

1. Attention is all you need;vaswani;Proc Adv Neural Inf Process Syst,2017

2. EMG Based Decoding of Object Motion in Dexterous, In-Hand Manipulation Tasks

3. The heat of shortening and the dynamic constants of muscle

4. Prototypical networks for few-shot learning;snell;arXiv 1703 05175,2017

5. Defending Smart Electrical Power Grids against Cyberattacks with Deep Q -Learning

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1. Enhanced Few-Shot Malware Traffic Classification via Integrating Knowledge Transfer With Neural Architecture Search;IEEE Transactions on Information Forensics and Security;2024

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