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% -> 20.34% and offline personalized CER from 10.86% -> 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.
@article{arxiv.2511.18213,
title = {Typing Reinvented: Towards Hands-Free Input via sEMG},
author = {Kunwoo Lee and Dhivya Sreedhar and Pushkar Saraf and Chaeeun Lee and Kateryna Shapovalenko},
journal= {arXiv preprint arXiv:2511.18213},
year = {2025}
}