A Novel Approach to Surface EMG-Based Gesture Classification Using a Vision Transformer Integrated With Convolutive Blind Source Separation
Author:
Affiliation:
1. Department of Biomedical Science and Engineering, Gwangju Institute of Science and Technology, Gwangju, South Korea
Funder
National Research Foundation of Korea
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics
Link
http://xplorestaging.ieee.org/ielx7/6221020/10380963/10308789.pdf?arnumber=10308789
Reference62 articles.
1. Surface Electromyography as a Natural Human–Machine Interface: A Review
2. Noninvasive Neural Interfacing With Wearable Muscle Sensors: Combining Convolutive Blind Source Separation Methods and Deep Learning Techniques for Neural Decoding
3. Advances and Disturbances in sEMG-Based Intentions and Movements Recognition: A Review
4. A review of the key technologies for sEMG-based human-robot interaction systems
5. Toward Robust, Adaptiveand Reliable Upper-Limb Motion Estimation Using Machine Learning and Deep Learning–A Survey in Myoelectric Control
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1. Explainable deep learning for sEMG-based similar gesture recognition: A Shapley-value-based solution;Information Sciences;2024-06
2. Signal to Image Conversion and Convolutional Neural Networks for Physiological Signal Processing: A Review;IEEE Access;2024
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