User Training With Error Augmentation for sEMG-Based Gesture Classification

User Training With Error Augmentation for sEMG-Based Gesture Classification

We designed and tested a system for real-time control of a user interface by extracting surface electromyographic (sEMG) activity from eight electrodes in a wristband configuration. sEMG data were streamed into a machine-learning algorithm that classified hand gestures in real-time. After an initial...

Full description

Bibliographic Details
Main Authors: Yunus Bicer, Niklas Smedemark-Margulies, Basak Celik, Elifnur Sunger, Ryan Orendorff, Stephanie Naufel, Tales Imbiriba, Deniz Erdogmus, Eugene Tunik, Mathew Yarossi
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Transactions on Neural Systems and Rehabilitation Engineering
Subjects:
Myoelectric control
gesture recognition
human–computer interaction
error augmentation
co-adaptation
surface electromyography (sEMG)
Online Access:https://ieeexplore.ieee.org/document/10457576/

Internet

https://ieeexplore.ieee.org/document/10457576/

Similar Items