Hydrogels hold substantial promise for use in intelligent wearable devices that can translate gestures into recognizable signals. However, the practical deployment of conventional hydrogels is hindered by their inadequate mechanical robustness and poor environmental stability. To overcome these limitations, this study functionalized reduced graphene oxide (rGO) through amino-group modification (NH2-rGO) and incorporated functionalized rGO and phytic acid (PA) into a polyvinyl alcohol (PVA) matrix to fabricate a PVA/PA/NH2-rGO composite conductive hydrogel. The effects of PA and NH2-rGO concentrations on the properties of the hydrogel were systematically investigated. The incorporation of a small amount of NH2-rGO markedly improved the mechanical performance of the PVA/PA4/NH2-rGO0.1 hydrogel, yielding a tensile strength of 0.7 MPa and an elongation at break of 305%. At the same time, dynamic hydrogen bonds within the network imparted the hydrogel with excellent cyclic durability. As non-toxic and environmentally benign components, NH2-rGO and PA also contributed to enhanced conductivity, resulting in the high strain sensitivity of the composite hydrogel. Under both small (8%) and large (268%) strains, the hydrogel showed pronounced changes in relative electrical resistance, corresponding to a gauge factor of 2.38. Furthermore, a wearable strain sensor based on the prepared hydrogel accurately recognized gestures. Through the integration with machine learning algorithms, we developed a sign-language translation system capable of recognizing multiple gestures with high precision. This work provides a solid foundation for next-generation intelligent sign-language recognition technologies and offers broad potential for applications in human-machine interfaces, smart wearables, and the Internet of Things.
Zhang et al. (2026) studied this question.