3D Bristle-Structured,\nKnitted-Fabric-Based Triboelectric\nSensors for Machine Learning-Based Motion Recognition

With the development of electronic technology, triboelectric-based\nsensors have been widely researched in fields such as healthcare,\nrehabilitation training, and sports assistance due to their manufacturing\nconvenience and self-powering advantages. Among them, 3D fabric-based\ntriboelectric sensors not only possess advantages such as easy mechanized\nproduction, good breathability, and ease of wearing but also their\nunique 3D structure enhances the specific surface area, thereby amplifying\nthe sensitivity. This study proposes a 3D bristle-structured fabric\nmade by a digital knitting technology that has not been studied widely\nfor triboelectric devices. By applying the 3D bristle structure with\na large specific surface area to the single jersey fabric, the effective\ncontact area during friction can be increased, resulting in a higher\nsurface charge density. Additionally, the microcapacitor-like effect\nprovided by the numerous microstructures allows the device to store\nmore surface charge, further improving the output performance. The\nstudy systematically investigates the output performance of four different\nstructures assembled by single jersey and 3D bristle-structured fabrics.\nThe optimal sample exhibits a 57% higher output voltage than that\nof the reference 2D fabric sample. The 3D bristle-structured fabric\ndemonstrates linear high sensitivity and distinct output performance\nwhen used as a sensor. Finally, a machine learning integration is\napplied to judge motion to assist a baseball pitcher in a self-training\nsystem.