High-Precision Sign Language Recognition Enabled by Self-Recoverable Near-Infrared Mechanoluminescent Materials

Intelligent evolution of human-machine interaction technology demands core capabilities in flexible sensors, including high stability, anti-interference properties, and self-powering. Traditional electrical sensors usually struggle to adapt to complex and long-term application scenarios. Mechanoluminescence (ML) materials offer a novel solution to this challenge, while existing ML materials still face issues such as the requirement for pre-radiation charging and insufficient cycling stability. Herein, this work developed a series of self-recoverable near-infrared (NIR) ML materials - ZnGa _{1 −  m} Al _m InO ₄ :Cr ³⁺ , which possess excellent piezoelectric properties, low cost and biocompatibility. By adjusting the doping concentration of Al ³⁺ ions, the crystal field strength of the material was precisely controlled, resulting in a 40.65-fold increase in photoluminescence intensity. Even after undergoing thousands of cycles of mechanical stimulation, the self-recoverable NIR ML material can still maintain 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 highway 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.