A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems-Reference-Cited by-同舟云学术

A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems

Published:2023-06-14 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Symposium on Medical Measurements and Applications (MeMeA)
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Author:

Kulwa Frank¹,Samuel Oluwarotimi Williams²,Asogbon Mojisola Grace¹,Oyemakinde Tolulope Tofunmi¹,Obe Olumide Olayinka³,Li Guanglin¹

Affiliation:

1. Shenzhen Institute of Artificial Intelligence and Robotics for Society, Shenzhen Institute of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS),CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems,Shenzhen,Guangdong,China,518055

2. University of Derby,School of Computing and Engineering,Derby,United Kingdom,DE22 3AW

3. Federal University of Technology,Department of Computer Science,Akure,Nigeria

Funder

Research and Development

National Natural Science Foundation of China

Ministry of Science and Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10171833/10171851/10171910.pdf?arnumber=10171910

Reference24 articles.

1. Spatial Correlation of High Density EMG Signals Provides Features Robust to Electrode Number and Shift in Pattern Recognition for Myocontrol

2. Influence of EMG-signal processing and experimental set-up on prediction of gait events by neural network

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

4. Combining Improved Gray-Level Co-Occurrence Matrix With High Density Grid for Myoelectric Control Robustness to Electrode Shift

5. Improved High-Density Myoelectric Pattern Recognition Control Against Electrode Shift Using Data Augmentation and Dilated Convolutional Neural Network

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