Deep learning–empowered triboelectric acoustic textile for voice perception and intuitive generative AI-voice access on clothing

Abstract

Integrating generative artificial intelligence (AI) chatbots with acoustic perception textiles allows everyday clothing to retrieve information, seek advice, and perform tasks through voice interactions. Here, we present a deep learning (DL)–empowered triboelectric AI acoustic textile (A-Textile) leveraging electrostatic charges on clothing for imperceptible, active voice perception and AI access. The multilayered A-Textile features a composite coating of three-dimensional SnS2 nanoflowers (NFs) embedded in silicone rubber to enhance charge capture and transfer, along with a SnS2 NFs–decorated graphite-like carbonized textile for charge accumulation and preservation. This design maximizes the charge density on the textile, achieving a 21-volt output, 1.2 volts per pascal sensitivity, 1-hertz resolution, and a wide sound response frequency range of 80 to 900 hertz. Using a well-trained DL model, the A-Textile precisely classifies and visualizes voice commands for internet-of- things control and cloud information access. Furthermore, we demonstrate its integration with ChatGPT, providing an intuitive interface for engaging with generative AI services to perform sophisticated tasks.