Ultraprecise Sign Language Recognition Realized by Self‐Recoverable Near‐Infrared Mechanoluminescent Materials

Abstract

ABSTRACT Advancements in human‐machine interaction technology require flexible sensors to possess core capabilities, including high stability, anti‐interference properties, and self‐powering functionality. Traditional electrical sensors usually struggle to adapt to complex and long‐term application scenarios. Mechanoluminescence (ML) materials present a novel solution to this challenge, yet existing ML materials still suffer from issues such as requiring pre‐radiation charging and insufficient cycling stability. Here, we report a series of self‐recovering near‐infrared (NIR) ML materials—ZnGa 1‐ m Al m InO 4 :Cr 3+ , which possess excellent piezoelectric properties and low cost. By precisely controlling the crystal field strength through adjusting the doping concentration of Al 3+ ions, the photoluminescence intensity was enhanced by 40.65‐fold. Even after undergoing thousands of mechanical stimulation cycles, this self‐healing near‐infrared ML material retains 98% of its initial luminescence intensity. When integrated with photoelectric sensors, ZAIO:Cr 3+ @PDMS demonstrated outstanding performance in sign language recognition (achieving 99.46% accuracy) and intelligent road monitoring through convolutional neural networks. This work provides novel insights for designing NIR ML materials and lays the foundation for integrating ML materials with intelligent neural networks.