Scale Production of a Stretchable Fiber Triboelectric Nanogenerator in Customizable Textile for Human Motion Recognition

Abstract

Although the fiber-based triboelectric nanogenerator (F-TENG) has been recognized as one of the most promising flexible sensor systems, it is facing a challenge of balancing the performance and the processing scalability. Herein, we develop a hierarchical coaxial F-TENG possessing PU layer, Ag layer, and PA layer from the core to the outer part by an efficient and straightforward two-step braiding method. Owning a small diameter of 1 mm, the F-TENG presents a high linear sensing response, a wide working range of 5 to 150 kPa, and a quick reaction speed of around 200 ms. In addition, it shows high flexibility, cyclic washability, and superior mechanical stability. Furthermore, customizable textiles (e.g., wrist support and socks) that conform perfectly to the human body have been knitted from the F-TENGs as warps or wefts, which are able to monitor human motion signals. Together with an optimized machine learning algorithm, five human motions (stand, slow walk, normal walk, run, and jump) can be analyzed with a precision of up to 99%. In short, this work presents a scalable approach to develop customizable self-powered sensing textiles, offering an excellent wearable digital platform/system for potential motion capture/monitoring, identification, and smart-sports-related applications.