Surfactant-Enhanced Anti-Swelling Hydrogel Flexible Sensor for Machine Learning-Assisted Underwater Gesture Recognition.

Abstract

Hydrogels hold great promise as flexible sensors. However, the development of hydrogel sensors with exceptional anti-swelling properties and stable performance remains a significant challenge. This study introduced a novel design strategy based on surfactant-assisted hydrophobic associative hydrogels. Through free-radical copolymerization in the presence of the surfactant cetyltrimethylammonium bromide, hydrogels with outstanding mechanical properties, excellent anti-swelling capabilities, and superior sensing performance are successfully fabricated. The resulting hydrogel demonstrated remarkable anti-swelling behavior (swelling ratio: -2.3%), exceptional mechanical strength (breaking strain: 3594.5%), and sustained fatigue resistance during repeated underwater loading-unloading cycles (1000 cycles). Integrating the hydrogel sensor with machine learning, precise and stable underwater gesture recognition and motion monitoring are achieved with an accuracy of 98.3%. This study provides a new perspective for advancing flexible underwater sensor technologies and underscores their broad potential in smart wearable devices, health monitoring, and underwater exploration.