Soft Active Electromyography Interface for Machine Learning-Enabled Silent Speech Recognition
Read the original on arXiv Machine Learning →The paper presents a soft, active electromyography (EMG) interface that enables word-level silent speech recognition (SSR) using machine learning. The device, worn on the hand, employs a fingertip electrode positioned near the lips to acquire EMG signals only when needed, and incorporates liquid metal interconnects, transparent flexible printed circuit electrodes, and elastomer encapsulation for mechanical stability during finger motion. A deep neural network trained on these stable signals achieved a mean accuracy of 97.2 ± 1.3 % across three subjects for a 30‑word vocabulary, and real‑time drone control demonstrated its practicality in noisy, privacy‑sensitive environments.
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