While myoelectric control has recently become a focus of increased research as a possible flexible hands-free input modality, current control approaches are prone to inadvertent false activations in real-world conditions. In this work, a novel myoelectric control paradigm -- on-demand myoelectric control -- is proposed, designed, and evaluated, to reduce the number of unrelated muscle movements that are incorrectly interpreted as input gestures . By leveraging the concept of wake gestures, users were able to switch between a dedicated control mode and a sleep mode, effectively eliminating inadvertent activations during activities of daily living (ADLs). The feasibility of wake gestures was demonstrated in this work through two online ubiquitous EMG control tasks with varying difficulty levels; dismissing an alarm and controlling a robot. The proposed control scheme was able to appropriately ignore almost all non-targeted muscular inputs during ADLs (>99.9%) while maintaining sufficient sensitivity for reliable mode switching during intentional wake gesture elicitation. These results highlight the potential of wake gestures as a critical step towards enabling ubiquitous myoelectric control-based on-demand input for a wide range of applications.

翻译：尽管肌电控制作为一种灵活的非接触式输入模态近期已成为研究热点，但现有控制方案在实际环境中容易产生无意识的误触发。本研究提出、设计并评估了一种新型肌电控制范式——按需肌电控制——以减少被错误识别为输入手势的非相关肌肉运动数量。通过利用唤醒手势的概念，用户能够在专用控制模式与休眠模式之间切换，从而有效消除日常活动中的无意触发。本研究通过两项不同难度的在线泛在肌电控制任务（闹钟解除与机器人控制）展示了唤醒手势的可行性。所提出的控制方案能够在日常活动中恰当地忽略几乎所有非目标肌肉输入（>99.9%），同时在用户有意触发唤醒手势时保持足够的灵敏度以实现可靠的模式切换。这些结果凸显了唤醒手势作为实现基于肌电控制的泛在按需输入的关键步骤，在广泛应用中具有重要潜力。