Improving the Robustness and Adaptability of sEMG-Based Pattern Recognition Using Deep Domain Adaptation
Author:
Affiliation:
1. Institute of Rehabilitation Engineering and Technology, University of Shanghai for Science and Technology, Shanghai, China
Funder
National Key R&D Program of China
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/ielx7/6221020/9945616/09854078.pdf?arnumber=9854078
Reference50 articles.
1. Advances and Disturbances in sEMG-Based Intentions and Movements Recognition: A Review
2. Deep Cross-User Models Reduce the Training Burden in Myoelectric Control
3. Unsupervised Domain Adversarial Self-Calibration for Electromyography-Based Gesture Recognition
4. Self-Recalibrating Surface EMG Pattern Recognition for Neuroprosthesis Control Based on Convolutional Neural Network
5. Surface EMG-Based Inter-Session Gesture Recognition Enhanced by Deep Domain Adaptation
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