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.

翻译：我们设计并测试了一套系统，通过提取腕带式配置中八个电极的表面肌电（sEMG）活动，实现对用户界面的实时控制。sEMG数据被输入机器学习算法，实时分类手势。在初始模型校准后，参与者在人类学习阶段接受三种反馈类型之一：真实反馈（手势分类算法的预测概率未经修改直接显示）、修改反馈（对这些概率应用隐藏的误差增强）以及无反馈。随后在一系列迷你游戏中对用户表现进行评估，要求受试者使用八种手势操控游戏虚拟角色完成任务。实验结果表明，与基线相比，修改反馈条件显著提升了准确率并改善了手势类别分离。这些发现表明，在游戏化用户界面中通过操纵反馈进行实时训练，能够为基于sEMG的手势识别应用实现直观、快速且准确的任务习得。