A Highly Flexible Self‐Powered Triboelectric Sensor Array for Silent Speech Recognition and Swallowing Motion Analysis

Abstract The growing prevalence of speech and swallowing disorders necessitates the development of advanced, non‐invasive technologies for effective communication and rehabilitation. Conventional silent speech recognition (SSR) methods, including vision‐based, ultrasound, inaudible acoustic, and surface electromyography (sEMG) approaches, suffer from limitations such as sensitivity to lighting conditions, occlusions, motion artifacts, and reliance on external power sources, restricting their applicability. Similarly, gold‐standard swallowing assessments, including videofluoroscopic swallowing study (VFSS) and flexible endoscopic evaluation of swallowing (FEES), are invasive and unsuitable for continuous monitoring. To address these limitations, we introduce a highly flexible, self‐powered tactile sensor array based on triboelectric nanogenerator (TENG) for SSR and swallowing motion analysis. The sensor comprises a microstructured polydimethylsiloxane (PDMS) layer and an electrospun Nylon 6/6 nanofiber film optimized for triboelectric charge generation and mechanical stability. Integrated within a 2×2 matrix, the TENG sensor array accurately captures lip and laryngeal movements. Machine learning analysis enables accurate silent speech‐based user authentication (97.06%) and high‐precision classification (98.04%) of critical swallow rehabilitation maneuvers, including the supraglottic swallow, Mendelsohn maneuver, and super‐supraglottic swallow. This TENG‐based sensor array offers a robust, non‐invasive, and self‐sustaining solution for real‐time speech and swallowing analysis, establishing a foundation for next‐generation wearable assistive technologies bridging clinical diagnostics and rehabilitation.