User Training with Error Augmentation for Electromyogram-based Gesture Classification
This work addresses the problem of enhancing user training for sEMG-based gesture recognition applications, offering incremental improvements in accuracy through feedback manipulation.
The study tackled improving user performance in electromyogram-based gesture classification by testing different feedback types during a human-learning stage, finding that modified feedback with hidden error augmentation significantly improved accuracy and gesture class separation compared to baseline.
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 feedback, in which we applied a hidden augmentation of error to these probabilities, and no feedback. User performance was then evaluated in a series of minigames, in which subjects were required to use eight gestures to manipulate their game avatar to complete a task. Experimental results indicated that, relative to baseline, the modified feedback condition led to significantly improved accuracy and improved gesture class separation. These findings suggest that real-time feedback in a gamified user interface with manipulation of feedback may enable intuitive, rapid, and accurate task acquisition for sEMG-based gesture recognition applications.