{"ID":2890044,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.19736","arxiv_id":"2507.19736","title":"LowKeyEMG: Electromyographic typing with a reduced keyset","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.","short_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...","url_abs":"https://arxiv.org/abs/2507.19736","url_pdf":"https://arxiv.org/pdf/2507.19736v1","authors":"[\"Johannes Y. Lee\",\"Derek Xiao\",\"Shreyas Kaasyap\",\"Nima R. Hadidi\",\"John L. Zhou\",\"Jacob Cunningham\",\"Rakshith R. Gore\",\"Deniz O. Eren\",\"Jonathan C. Kao\"]","published":"2025-07-26T01:41:58Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"eess.SP\"]","methods":"[\"Transformer\",\"Language Model\"]","has_code":false}