Chenani, HosseinHosseinChenaniRazaghi, ZahraZahraRazaghiSaeidi, MohsenMohsenSaeidiAghaii, Amir HosseinAmir HosseinAghaiiRastkhiz, MahsaSadat AdelMahsaSadat AdelRastkhizOrouji, MinaMinaOroujiEkrami, AliakbarAliakbarEkramiSimchi, AbdolrezaAbdolrezaSimchi2025-02-062025-02-062025-01-25https://publica.fraunhofer.de/handle/publica/48371010.1016/j.talanta.2025.127640Real-time monitoring of sweat using wearable devices faces challenges such as limited adhesion, mechanical flexibility, and accurate detection. In this work, we present a stretchable, adhesive, bilayer hydrogel-based patch designed for continuous monitoring of sweat pH and glucose levels using AI-assisted smartphones. The patch is composed of a bottom PVA hydrogel layer functionalized with colorimetric reagents and glucose oxidase enzyme, while the top PVA-sucrose layer enhances skin adhesion and protects against air moisture. The hydrogel demonstrates excellent mechanical properties with a tensile strain of 440 % and an elastic modulus of 157 kPa, providing a strong yet flexible interface with the skin. Machine learning models, including random forest (RF) and convolutional neural network (CNN), enabled accurate sweat analysis, achieving a coefficient of determination (R2) of ~0.99 for pH (3-9) and glucose concentrations up to 0.5 mM. Validation against standard methods like HPLC confirmed the reliability of the patch. This AI-powered system offers a promising platform for next-generation wearable health monitoring devices.enWearable colorimetric biosensorPVA/PVA-Sucrose hydrogelContinuous glucose detectionSweat analysisMachine learning500 Naturwissenschaften und Mathematikjournal article