Abstract: With the advancement of artificial intelligence (AI) and metaverse technologies, wearable triboelectric nanogenerator (TENG)-based hand gesture sensors have become pivotal for natural human-computer interaction (HCI). This review systematically summarizes the latest progress of wearable TENG hand gesture sensors from a fabrication method perspective. First, it outlines the significance of hand gesture recognition, comparing vision-based and sensor-based approaches, and highlights TENGs' advantages as self-powered sensors. Then, this discussion addresses the role of in-situ characterization and first-principles calculations in the understanding and optimization of TENG materials, fabrication processes, and application mechanisms, while categorizing TENG sensors into four groups based on fabrication methods: dip-coating/solution casting, solution-based/molding, printing/spinning, and etching/deposition. Each category is discussed in terms of fabrication processes, material selection, structural features, and application cases, such as textile-based gloves via dip-coating, microstructured sensors via molding, automated devices via 3D printing/electrospinning, and high-precision sensors via etching/deposition. Finally, key challenges are analyzed, including uneven coating, high equipment cost, and breathability-durability trade-offs. Future perspectives are proposed, including hybrid fabrication for scalability, sustainable/natural materials for eco-friendliness, structural optimization for wear comfort, and intelligent integration (multimodal sensing, AI) for expanded scenarios. This review provides critical insights for developing high-performance, commercializable TENG hand gesture sensors to advance HCI, healthcare, and smart technologies.

Key words: Triboelectric nanogenerator, Fabrication methods, Wearable sensors, Gesture recognition