Filtering noise at the source
Rather than relying solely on software to clean up noisy data, the team tackled the problem at the sensor-body interface itself. The metahydrogel artefact-mitigating platform (MAP) combines two filtering mechanisms in a single material. Nanoparticles self-assembled into periodic bands within the hydrogel scatter and absorb mechanical vibrations, much like how a soundproofing panel traps sound energy, blocking movement noise within targeted frequency ranges. At the same time, a biocompatible glycerol-water electrolyte controls how quickly ions travel through the gel, letting low-frequency heart signals (below 30 Hz) pass through, while suppressing higher-frequency muscle electrical noise. A machine-learning denoising algorithm then removes any remaining unstructured noise while preserving critical physiological features.
The platform is soft enough to match the mechanical properties of biological tissue, breathable with a water vapour transmission rate exceeding that of human skin and durable under repeated stretching. By combining improved hardware with smart algorithms, the system made the ECG signal much cleaner, boosting signal quality from 5.19 dB to 37.36 dB. This clearer signal helps it detect key ECG peaks more reliably, raising peak-detection accuracy from 52 per cent to 93 per cent and making it easier to tell fatigue-related patterns from normal heart rhythms.
“Compared with current commercial devices, our metahydrogel platform demonstrates superior performance, particularly under motion conditions where artefact suppression is critical. Current smartwatches typically achieve ECG signal-to-noise ratios of 10-20 dB, which can decrease by approximately 40 per cent under motion due to artefacts and unstable contact. Our system achieves around 37 dB during daily activities,” said Dr Tian.
From stable signals to mental-state decoding
Because fatigue disrupts the autonomic nervous system, it leaves measurable traces in heart rate variability, blood pressure patterns and ECG waveform features – but only if those signals can be captured cleanly during everyday activity. The team built a fully integrated, flexible wearable MAP system with wireless transmission and used it to monitor participants over multiple days, including simulated driving tasks designed to induce fatigue.
Using high-quality cardiovascular data collected from the hydrogel sensor, a deep-learning system identified fatigue levels with 92 per cent accuracy, versus 64 per cent when trained on data collected without MAP. The team also showed that the system meets the ISO 81060-2 gold-standard requirements for blood pressure monitoring.
Beyond fatigue tracking, MAP suppressed artefact across diverse biosignal types, including heart sounds, respiratory sounds, voice, brain-wave and eye-movement recordings, highlighting its potential for broader neurophysiological and mental health monitoring.