Abstract
As the Internet of Things advances,gesture recognition emerges as a prominent domain in human-machine interaction(HMI).However,interactive wearables based on conductive hydrogels for individuals with single-arm functionality or disabilities remain underexplored.Here,we devised a wearable one-handed keyboard with gesture recognition,employing machine learning algorithms and hydrogel-based mechanical sensors to boost productivity.PCG(PAM/CMC/rGO)hydrogels are composed of polyacrylamide(PAM),sodium carboxymethyl cellulose(CMC),and reduced graphene oxide(rGO),which function as a strain,pressure sensor,and electrode material.The PAM chains offer the gel's elasticity by covalent cross-linking,while the biocompatible CMC improves the dispersion of rGO and promotes electromechanical properties.Integrating rGO sheets into the polymer matrix facilitates cross-linking and generates supple-mentary conductive pathways,thereby augmenting the gel system's elasticity,sensitivity,and durability.Our hydrogel sensors include high sensitivity(gage factor(GF)=8.18,395.6％-551.96％)and superior pressure sensing capabilities(Sensitivity(S)=0.3116 kPa-1,0-9.82 kPa).Furthermore,we developed a wearable keyboard with up to 98.13％accuracy using convolutional neural networks and a custom data acquisition system.This study establishes the groundwork for creating multifunctional gel sensors for intelligent machines,wearable devices,and brain-computer interfaces.
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