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We designed and tested a system for real-time control of a user interface byextracting surface electromyographic (sEMG) activity from eight electrodes in awrist-band configuration. sEMG data were streamed into a machine-learningalgorithm that classified hand gestures in real-time. After an initial modelcalibration, participants were presented with one of three types of feedbackduring a human-learning stage: veridical feedback, in which predictedprobabilities from the gesture classification algorithm were displayed withoutalteration, modified feedback, in which we applied a hidden augmentation oferror to these probabilities, and no feedback. User performance was thenevaluated in a series of minigames, in which subjects were required to useeight gestures to manipulate their game avatar to complete a task. Experimentalresults indicated that, relative to baseline, the modified feedback conditionled to significantly improved accuracy and improved gesture class separation.These findings suggest that real-time feedback in a gamified user interfacewith manipulation of feedback may enable intuitive, rapid, and accurate taskacquisition for sEMG-based gesture recognition applications.

User Training with Error Augmentation for Electromyogram-based Gesture Classification