High-performance polydopamine and silver nanoparticle-modified MXene-based hydrogel flexible strain sensors for transfer-learning-assisted handwriting recognition and wrist-movement monitoring

Abstract

Hydrogel-based flexible strain sensors show great potential for applications in wearable electronics, human–machine interfaces, and soft robotics. However, it remains challenging to prepare hydrogel sensors with high sensitivities and wide detection ranges. Herein, we report a polydopamine and silver nanoparticle-modified MXene-based hydrogel, namely, MXene/Ag@PDA/P(AA-AM)/SA-Zn (MAPS), which exhibits a high sensitivity (with a gauge factor of 16.64), high stretchability (1100%), and short response time (0.2 s). This hydrogel-based strain sensor can be adhered to the human body surface as a wearable sensor to monitor physiological and motion signals from small to large strains in a comprehensive range (0.2%–750%). Assisted by the ResNet50D algorithm, a new hydrogel-based handwriting recognition system is developed, which is able to recognize the content of handwritten signals with high accuracy (98.6%) and a short recognition time (less than 1 s). Moreover, we design a multi-directional strain sensor consisting of three independent MAPS hydrogel-based sensor units for effectively distinguishing multidimensional wrist movements. This study offers a promising strategy to prepare a high-performance hydrogel-based strain sensor, showing potential applications in the field of intelligent sensing, human-motion monitoring, and human–computer interaction.