Intrinsic Motion-Artifact Filtering through a Viscoelastic Conductive Hydrogel Interface for Wearable ECG Applications

Microstructural relaxation–damping mechanism explains <20   Hz noise attenuation. • Rheo-electrical impedance coupling links strain dissipation and signal stability. • Material–signal co-design framework enables adaptive high-fidelity biosensing. Reliable electrocardiogram (ECG) monitoring during daily activities remains challenging due to motion artifacts from body movement and respiration, predominantly below 20   Hz, which distort baseline stability and obscure key ECG waveform features such as the T-wave and ST-segment. Conventional approaches often rely on hardware or algorithmic filtering, which can compromise signal fidelity. Here, we present a viscoelastic conductive hydrogel interface designed to reduce motion-induced interference during ECG acquisition by combining low-frequency mechanical energy dissipation with interfacial electrical stabilization. The hydrogel (PGHM) comprises polyacrylamide (PAAm), gum arabic (GA), hydrolyzed keratin (HK), and MXene, forming a semi-interpenetrating double network that integrates mechanical adaptability with high electrical conductivity. The GA–HK network exhibits a relaxation window at 10-20   Hz, which may contribute to frequency-dependent dissipation of motion-induced mechanical perturbations within the artifact-relevant frequency range, while the MXene percolation network supports continuous charge transport and stable impedance matching at the skin–electrode interface. Additionally, signal processing techniques validated the hydrogel’s performance, resulting in an approximately 12.7   dB improvement in signal-to-noise ratio and better preservation of waveform fidelity under motion, outperforming commercial Ag/AgCl electrodes. This work illustrates a materials–signal co-design framework, offering a generalizable strategy for intrinsic artifact suppression in dynamic, biocompatible bioelectronic interfaces, with potential applications in wearable health monitoring systems. Download: Download high-res image (192KB) Download: Download full-size image