{"ID":2838373,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2511.18213","arxiv_id":"2511.18213","title":"Typing Reinvented: Towards Hands-Free Input via sEMG","abstract":"We explore surface electromyography (sEMG) as a non-invasive input modality for mapping muscle activity to keyboard inputs, targeting immersive typing in next-generation human-computer interaction (HCI). This is especially relevant for spatial computing and virtual reality (VR), where traditional keyboards are impractical. Using attention-based architectures, we significantly outperform the existing convolutional baselines, reducing online generic CER from 24.98% -\u003e 20.34% and offline personalized CER from 10.86% -\u003e 10.10%, while remaining fully causal. We further incorporate a lightweight decoding pipeline with language-model-based correction, demonstrating the feasibility of accurate, real-time muscle-driven text input for future wearable and spatial interfaces.","short_abstract":"We explore surface electromyography (sEMG) as a non-invasive input modality for mapping muscle activity to keyboard inputs, targeting immersive typing in next-generation human-computer interaction (HCI). This is especially relevant for spatial computing and virtual reality (VR), where traditional keyboards are impracti...","url_abs":"https://arxiv.org/abs/2511.18213","url_pdf":"https://arxiv.org/pdf/2511.18213v1","authors":"[\"Kunwoo Lee\",\"Dhivya Sreedhar\",\"Pushkar Saraf\",\"Chaeeun Lee\",\"Kateryna Shapovalenko\"]","published":"2025-11-22T23:04:45Z","proceeding":"cs.HC","tasks":"[\"cs.HC\",\"cs.LG\"]","methods":"[]","has_code":false}