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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2511.18213 |
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| _version_ | 1866911282704154624 |
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| author | Lee, Kunwoo Sreedhar, Dhivya Saraf, Pushkar Lee, Chaeeun Shapovalenko, Kateryna |
| author_facet | Lee, Kunwoo Sreedhar, Dhivya Saraf, Pushkar Lee, Chaeeun Shapovalenko, Kateryna |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_18213 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Typing Reinvented: Towards Hands-Free Input via sEMG Lee, Kunwoo Sreedhar, Dhivya Saraf, Pushkar Lee, Chaeeun Shapovalenko, Kateryna Human-Computer Interaction Machine Learning 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. |
| title | Typing Reinvented: Towards Hands-Free Input via sEMG |
| topic | Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2511.18213 |