LowKeyEMG: Electromyographic typing with a reduced keyset

TL;DR

LowKeyEMG is a real-time EMG-based text entry system using only 7 gestures, achieving practical typing speeds and high accuracy, suitable for assistive tech and low-bandwidth interfaces.

Contribution

This work introduces a novel low-key EMG-based typing interface with a reduced gesture set and a transformer language model, enabling reliable and efficient text entry.

Findings

Average typing speed of 23.3 words per minute.

Achieved 98.2% top-3 word accuracy.

Improved gesture efficiency by 17%.

Abstract

We introduce LowKeyEMG, a real-time human-computer interface that enables efficient text entry using only 7 gesture classes decoded from surface electromyography (sEMG). Prior work has attempted full-alphabet decoding from sEMG, but decoding large character sets remains unreliable, especially for individuals with motor impairments. Instead, LowKeyEMG reduces the English alphabet to 4 gesture keys, with 3 more for space and system interaction, to reliably translate simple one-handed gestures into text, leveraging the recurrent transformer-based language model RWKV for efficient computation. In real-time experiments, participants achieved average one-handed keyboardless typing speeds of 23.3 words per minute with LowKeyEMG, and improved gesture efficiency by 17% (relative to typed phrase length). When typing with only 7 keys, LowKeyEMG can achieve 98.2% top-3 word accuracy, demonstrating that this low-key typing paradigm can maintain practical communication rates. Our results have implications for assistive technologies and any interface where input bandwidth is constrained.