Meta-topological hydrogel enables multisource and frequency-tailored artefact mitigation for bioelectronics

High-fidelity signal acquisition underpins next-generation healthcare bioelectronics, yet motion artefacts severely impair both signal integrity and measurement reliability. Existing mitigation strategies primarily target a single artefact type or a fixed frequency range, limiting scalability and generality. Here we report a meta-topological hydrogel that combines programmable phononic metastructure filtering with topology-tunable ion diffusion to suppress multisource mechanical and biopotential artefacts across tailored frequency ranges. This artefact-mitigating platform enables simultaneous, artefact-free acquisition of haemodynamic and electrophysiological signals, achieving ISO-grade A blood pressure accuracy and an electrocardiograph signal-to-noise ratio of 37.36 dB during daily activities. The platform supports robust feature extraction from physiological signals for fatigue profiling, achieving a deep learning classification accuracy of 92.04%. We further demonstrate effective artefact suppression across diverse biosignals modalities, including heart and respiratory sounds, voice, electroencephalogram and electrooculogram, highlighting its potential for scalable and kinematic-tolerant monitoring in motion-intensive scenarios. A meta-topological hydrogel suppresses multisource motion artefacts across tailored frequencies, enabling artefact-free haemodynamic and electrophysiological recording with robust fatigue profiling.