Ultrasensitive textile strain sensors redefine wearable silent speech interfaces with high machine learning efficiency

This work introduces a silent speech interface (SSI) using a few-layer graphene (FLG) strain sensing mechanism based on thorough cracks and AI-based self-adaptation. It achieves high accuracy (95.25%), high computational efficiency (90% reduction in computational load), and fast decoding speed while maintaining user comfort. A textile-integrated ultrasensitive strain sensor embedded in a smart choker captures subtle throat movements, enabling a computationally efficient 1D convolutional neural network for speech decoding. The synergy of sensor design and neural network optimization offers a promising solution for practical, wearable SSI systems.

Chenyu Tang, Muzi Xu, Wentian Yi, Zibo Zhang, Edoardo Occhipinti, Chaoqun Dong, Dafydd Ravenscroft, Sung-Min Jung, Sanghyo Lee, Shuo Gao, Jong Min Kim, Luigi Giuseppe Occhipinti