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，通过提取八通道腕带式配置下表面肌电活动，实现用户界面的实时控制。肌电数据被实时输入机器学习算法，用于对手势进行分类。在初始模型校准后，参与者在人类学习阶段获得三种反馈类型之一：真实反馈（手势分类算法预测概率未经修改直接显示）、修正反馈（对这些概率应用隐藏的误差增强），以及无反馈。随后通过一系列小游戏评估用户表现，受试者需使用八种手势操控游戏角色完成任务。实验结果表明，与基线条件相比，修正反馈条件显著提升了分类准确率并改善了手势类别分离度。这些发现表明，在游戏化用户界面中通过操控实时反馈，可促进基于肌电手势识别应用实现直观、快速且精准的任务掌握。