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

## TL;DR

This paper introduces a 3D-printed hydrogel that can sense both movement and muscle signals, enabling accurate sign language recognition.

## Contribution

The development of a tunable 3D-printed ion-conductive hydrogel for multimodal sign language recognition.

## Key findings

- The hydrogel exhibits low hysteresis (90.25% recovery ratio) and high elongation (550%) for strain/pressure sensing.
- The system achieved 99.65% classification accuracy for 24 Chinese sign language gestures using a Bi-LSTM model.

## Abstract

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.

This work successfully fabricated ion‐conductive hydrogels with low hysteresis and high conductivity using 3D printing technology. By adjusting the component ratios, the properties of the hydrogels can be tuned to meet diverse sensing requirements. Finally, a multimodal sensing sign language recognition system was constructed based on this hydrogel, achieving accurate recognition of sign language.