3D‐Printed Ion‐Conductive Hydrogels with Tunable Mechanical–Electrical Properties for Multimodal Sign Language Recognition

Sign language recognition technology holds significant importance for eliminating communication barriers faced by the hearing-impaired population. To address the limitations of current wearable sensors‒such as complex fabrication or materials incompatibility within an integrated system, this study designed a 3D printed ion-conductive hydrogel with tunable electromechanical performances for versatile wearable sensing. The hydrogel is primarily based on a polyampholyte network interpenetrated with a polyacrylamide (PAAM) framework and synergistically integrated with LiCl and a covalent organic framework (COF) to enhance its electromechanical performance. It exhibits low hysteresis (90.25% recovery ratio) with high elongation (550%) and large compressive strain tolerance (90%) for strain/pressure sensing, while its low modulus (0.09 MPa) and high conductivity (0.23 S m^- ¹) enabled high-fidelity surface electromyography (sEMG) sensing. Leveraging these multifunctional hydrogels, we developed a multimodal sign language recognition system consisting of a pair of digital gloves, each embedded with 12 strain sensors and 5 pressure sensors, together with a flexible armband integrated with a 10-channel differential sEMG electrode array. Coupled with a bidirectional long short-term memory (Bi-LSTM) multimodal fusion model, the system achieved a classification accuracy of 99.65% across 24 Chinese sign language gestures.