Semi-supervised adaptation of upper-limb myoelectric pattern recognition prosthesis control through virtual gameplay
Author:
Affiliation:
1. Northwestern University,Center for Robotics and Biosystems,Evanston,IL,USA
2. Research and Grants Coapt, LLC,Chicago,IL,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10123380/10123710/10123898.pdf?arnumber=10123898
Reference10 articles.
1. Proportional Myoelectric Control of Robots: Muscle Synergy Development Drives Performance Enhancement, Retainment, and Generalization
2. Mobile, Game-Based Training for Myoelectric Prosthesis Control
3. Prosthetic Myoelectric Control Strategies: A Clinical Perspective
4. Machine Learning to improve pattern recognition control of upper-limb myoelectric prostheses;cummins;Proc Of MyoElectric Controls Symp (MEC) Fredericton,2017
5. Improving the Robustness of Myoelectric Pattern Recognition for Upper Limb Prostheses by Covariate Shift Adaptation
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