Natural Typing Recognition via Surface Electromyography
This work addresses gesture recognition for natural computer typing, enabling text reconstruction from muscle signals, which is incremental as it builds on existing methods with a novel dataset and architecture.
The researchers tackled the problem of reconstructing typed text from muscle potentials using surface electromyography, achieving over 90% character-level accuracy by building the largest existing dataset and employing deep learning methods inspired by real-time spoken language transcription.
By using a computer keyboard as a finger recording device, we construct the largest existing dataset for gesture recognition via surface electromyography (sEMG), and use deep learning to achieve over 90% character-level accuracy on reconstructing typed text entirely from measured muscle potentials. We prioritize the temporal structure of the EMG signal instead of the spatial structure of the electrode layout, using network architectures inspired by those used for real-time spoken language transcription. Our architecture recognizes the rapid movements of natural computer typing, which occur at irregular intervals and often overlap in time. The extensive size of our dataset also allows us to study gesture recognition after synthetically downgrading the spatial or temporal resolution, showing the system capabilities necessary for real-time gesture recognition.