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User Training with Error Augmentation for Electromyogram-based Gesture Classification
March 26, 2024, 4:45 a.m. | Yunus Bicer, Niklas Smedemark-Margulies, Basak Celik, Elifnur Sunger, Ryan Orendorff, Stephanie Naufel, Tales Imbiriba, Deniz Erdo\u{g}mu\c{s}, Eugene
cs.LG updates on arXiv.org arxiv.org
Abstract: 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 wrist-band configuration. sEMG data were streamed into a machine-learning algorithm that classified hand gestures in real-time. After an initial model calibration, participants were presented with one of three types of feedback during a human-learning stage: veridical feedback, in which predicted probabilities from the gesture classification algorithm were displayed without alteration, modified …
abstract algorithm arxiv augmentation classification control cs.hc cs.lg data eess.sp error gestures machine real-time surface training type
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