Deep-learning-enabled breathable thermogalvanic hydrogel array for self-powered mental monitoring

Abstract

Facial expression is vital for assessing psychological health, especially in adolescents. Current facial expression recognition systems face challenges such as poor breathability, limited electromechanical performance, and insufficient environmental stability. Here, an air-permeable self-powered hydrogel array designed for continuous mental monitoring based on facial expression recognition is proposed. The remarkable breathability of the patch composed of thermogalvanic hydrogel and gelatin films is achieved by the through-holes structure design. Additionally, the hydrogel leverages a double network structure and phytic acid with rich hydrogen ions, achieving a trade-off between mechanical (1.07 MPa) and electrical (34.4 mS cm⁻¹) properties. Due to the multiple hydrogen bonds among phytic acid, glycerol, and water, the hydrogel maintains over 80% of its original electrical performance after 10 days, demonstrating its excellent environmental stability. Integrated with a deep learning algorithm, the hydrogel array can recognize six facial expressions with a high accuracy of 100% and provide long-term monitoring of mental states based on the positive-to-negative emotion ratio. This work paves the way for a new generation of wearable mental monitoring platform for healthcare and human–machine interfaces.