用手部柔性电极捕捉无声说话信号，实现高精度实时控制
Soft Active Electromyography Interface for Machine Learning-Enabled Silent Speech Recognition
- 用可穿戴手部电极在需要时采集唇部肌电信号
- 30词词汇识别准确率达97.2%，跨三名受试者稳定
- 适合噪声环境或需隐私保护的智能设备交互
无声语音识别（SSR）为无声音语境提供替代通信路径。但传统方法受限于面部持续贴附、隐私问题及信号不稳定。本文提出一种柔软、主动式肌电图（EMG）接口，结合机器学习实现词级SSR。设备佩戴于手部，通过指尖电极在需要时贴近嘴唇采集肌电信号。系统采用液态金属互连、透明柔性印刷电路电极和弹性体封装，确保手指运动中的高机械稳定性。基于这些稳定信号训练的深度神经网络，在三名受试者上对30词词汇集的平均分类准确率达到97.2%±1.3%，展现强语言区分能力。此外，实时无人机控制验证了该方法在嘈杂及隐私敏感环境中优于传统语音识别的实用性。本研究凸显了软性可穿戴EMG系统作为安全、直观人机接口的潜力。
原文摘要 · Abstract (English)
Silent speech recognition (SSR) provides an alternative communication pathway in the absence of audible speech. However, conventional approaches are limited by the need for constant facial attachment, privacy concerns, and unstable signal acquisition. Here, we propose a soft, active electromyography (EMG) interface that enables word-level SSR using machine learning. Worn on the hand, the device uses a fingertip electrode that can be positioned near the lips to acquire EMG signals only when needed. The interface integrates liquid metal (LM) interconnects, transparent flexible printed circuit (FPC) electrodes, and elastomer encapsulation to ensure high mechanical stability during finger motion. A deep neural network trained on these stable signals achieved a mean accuracy of 97.2 $\pm$ 1.3% across three subjects in classifying a 30-word vocabulary, demonstrating robust linguistic discrimination. Furthermore, real-time drone control validates the practicality of this approach in noisy and privacy-sensitive environments where conventional voice recognition fails. This study highlights the potential of soft, wearable EMG systems as secure and intuitive human-machine interfaces.
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