ArmIn: Explore the Feasibility of Designing a Text-entry Application Using EMG Signals

EMG is becoming an emerging interface for human-computer interface and has been applied to gesture recognition in previous work. However, those existing EMG-based interfaces can only recognize gestures at a coarse-grained level such as hand and arm gestures, which constraints their usage in applications involving fine-grained activities such as text entry via keystrokes. As a result, in this paper, we attempt to push the limit of existing EMG-based interfaces and propose the first wearable text-entry system, named ArmIn, with EMG signals. ArmIn is designed to recognize keystroke gestures with the help of a finger on printed and physical keyboards. We implement ArmIn using commodity EMG sensors and custom hardware board, and conduct experiments to evaluate its performance. By carefully designing the data processing scheme, ArmIn can recognize keystrokes on both kinds of keyboard, with 89.5% and 87.5% accuracy respectively, when it is worn on a user's left arm.