标题： LowKeyEMG：使用减少的键集进行肌电图分类 显示英文标题

标题： LowKeyEMG: Electromyographic typing with a reduced keyset

摘要： 我们介绍了LowKeyEMG，这是一种实时人机接口，仅使用从表面肌电图（sEMG）解码的7种手势类别即可实现高效的文本输入。 先前的研究尝试从sEMG中解码完整字母表，但解码大字符集仍然不可靠，尤其是对于运动障碍的个体。 相反，LowKeyEMG将英语字母表减少到4种手势键，再加上3种用于空格和系统交互，以可靠地将简单的单手手势转换为文本，利用基于循环变压器的语言模型RWKV进行高效计算。 在实时实验中，参与者使用LowKeyEMG实现了平均23.3词/分钟的单手无键盘打字速度，并将手势效率提高了17%（相对于输入短语长度）。 当仅使用7个键时，LowKeyEMG可以达到98.2%的前三词准确率，证明这种低键输入范式可以保持实际的通信速率。 我们的结果对辅助技术和任何输入带宽受限的界面都有意义。 显示英文摘要

摘要： We introduce LowKeyEMG, a real-time human-computer interface that enables efficient text entry using only 7 gesture classes decoded from surface electromyography (sEMG). Prior work has attempted full-alphabet decoding from sEMG, but decoding large character sets remains unreliable, especially for individuals with motor impairments. Instead, LowKeyEMG reduces the English alphabet to 4 gesture keys, with 3 more for space and system interaction, to reliably translate simple one-handed gestures into text, leveraging the recurrent transformer-based language model RWKV for efficient computation. In real-time experiments, participants achieved average one-handed keyboardless typing speeds of 23.3 words per minute with LowKeyEMG, and improved gesture efficiency by 17% (relative to typed phrase length). When typing with only 7 keys, LowKeyEMG can achieve 98.2% top-3 word accuracy, demonstrating that this low-key typing paradigm can maintain practical communication rates. Our results have implications for assistive technologies and any interface where input bandwidth is constrained.