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

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- _mAl_mInO₄:Cr³⁺, which possess excellent piezoelectric properties and low cost. By precisely controlling the crystal field strength through adjusting the doping concentration of Al³⁺ 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:Cr³⁺@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.