Dual-Modal Motion Monitoring via an rGO-Nanocomposite Hydrogel Electrode with a Tunable Hydrogen-Bond Network

Hydrogel-based bioelectrodes are emerging as next-generation platforms for wearable electronics owing to their skin-like softness, biocompatibility, and mixed ionic-electronic conductivity. However, achieving an optimal balance of mechanical compliance, adhesion, and conductivity for stable surface electromyography (sEMG) monitoring under dynamic conditions remains a significant challenge. Herein, we report a PVA-HEDP-HPAA-rGO (PHHrGO) hydrogel that synergistically integrates dual-molecule hydrogen-bond regulation with reduced graphene oxide reinforcement to deliver a skin-conformal modulus (5-50 kPa), adjustable adhesion (∼2.2 N), and enhanced conductivity (>13 S/m). The optimized hydrogel electrodes exhibit low interfacial impedance, significantly outperforming commercial Ag/AgCl electrodes, and maintain a >95% signal-to-noise ratio even after 20 reuse cycles. Applied as flexible sEMG sensors, PHHrGO hydrogel electrodes enable precise discrimination of finger, wrist, arm, and thigh motions via linear discriminant analysis and hierarchical cluster analysis. Furthermore, using a three-channel setup with nine extracted features, an artificial neural network achieves 100% accuracy in recognizing five gestures. This work developed a material-algorithm coengineering framework that bridges hydrogen-bond network design and machine learning analytics, providing a versatile platform for prosthetic control, human-machine interaction, and rehabilitation monitoring.