用肌肉信号实现无手输入，让虚拟现实打字更自然。
Typing Reinvented: Towards Hands-Free Input via sEMG
- 用注意力模型解析肌电信号，映射为键盘输入
- 离线个性化识别错误率降至10.10%，在线通用模式降为20.34%
- 轻量级语言纠错设计，适合可穿戴和空间计算场景
我们探索表面肌电（sEMG）作为非侵入式输入方式，将肌肉活动映射为键盘输入，以支持下一代人机交互中的沉浸式打字。该技术对空间计算和虚拟现实尤为重要，因传统键盘在此类场景中不实用。采用基于注意力的架构，显著优于现有卷积基线：在线通用字符错误率（CER）从24.98%降至20.34%，离线个性化CER从10.86%降至10.10%，同时保持完全因果性。进一步引入轻量级解码流水线，结合语言模型纠错，验证了高精度、实时肌电驱动文本输入在可穿戴与空间界面中的可行性。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。