  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.
