User Training with Error Augmentation for Electromyogram-based Gesture Classification

TL;DR

This study introduces a real-time sEMG-based gesture control system that uses error augmentation feedback to enhance user learning, resulting in improved accuracy and gesture separation in a gamified interface.

Contribution

The paper presents a novel feedback method involving error augmentation during real-time sEMG gesture training, improving user performance over traditional feedback.

Findings

Error-augmented feedback significantly improves gesture classification accuracy.

Participants showed better gesture separation with error augmentation.

Enhanced user learning demonstrated in gamified tasks.

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 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.