Typing Reinvented: Towards Hands-Free Input via sEMG
This addresses the need for practical text input in spatial computing and virtual reality where traditional keyboards are unavailable, representing an incremental improvement over existing methods.
The paper tackles the problem of hands-free typing for immersive human-computer interaction by using surface electromyography (sEMG) to map muscle activity to keyboard inputs, achieving reductions in character error rates from 24.98% to 20.34% for online generic typing and from 10.86% to 10.10% for offline personalized typing.
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.