About one in three employees in Singapore report feeling burnt out — one of the highest rates globally. Burnout and chronic fatigue carry a substantial economic cost and pose serious risks in professions where alertness is critical. Yet diagnosing fatigue and related mental health conditions today relies largely on self-reported questionnaires, which tend to be subjective, intermittent and poorly suited to real-time evaluation.
A research team led by Professor Ho Ghim Wei from the Department of Electrical and Computer Engineering under the College of Design and Engineering at the National University of Singapore, with Research Fellow Dr Tian Guo as first author, has developed a metahydrogel platform integrated with AI-driven signal processing that suppresses multiple sources of motion noise simultaneously. The system delivers an electrocardiograph (ECG) signal-to-noise ratio (SNR) of 37.36 dB and blood pressure deviation as low as 3 mmHg during movement — accuracy that meets ISO clinical-grade standards and outperforms commercial trackers currently available in the market. Combined with machine learning, the platform classifies fatigue levels with 92 per cent accuracy, pointing towards objective, continuous mental health monitoring in real-world settings.
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.
“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.
“We hope to work closely with mental-health physicians to better understand what types of physiological data are most relevant in real-world settings, as well as the level of accuracy required to meet clinical needs. Clinicians can provide valuable insights to help us establish meaningful links between the data and pathological conditions,” said Prof Ho. “Our current material synthesis and system fabrication are still largely based on laboratory processes. We aim to collaborate with industrial partners to optimise manufacturing strategies and advance the platform toward practical, product-level implementation,” she added.
The team’s findings were published in the scientific journal Nature Sensors on 24 March 2026.