Machine-Learning-Enabled Hydrogel Biosensors for Wearable Health Monitoring.

Overview

1. School of Engineering, The University of Manchester, Manchester M13 9PL, UK
2. Faculty of Engineering, University College London, London WC1E 6BT, UK

Abstract

Machine learning (ML) is reshaping the design and deployment of conductive hydrogel biosensors for wearable health monitoring by coupling material chemistry with scalable manufacturing and robust signal analytics. Persistent bottlenecks include hydration stability (dehydration and freezing), data scarcity, device variability, and model transfer across users and environments. Recent advances demonstrate ML-enabled gains across electrochemical, mechanical, optical, and multimodal transduction, improving feature extraction, drift compensation, and generalization in applications spanning electrophysiology, sweat chemistry, and soft tactile sensing. On the material side, polymer informatics and graph-based representations are emerging to predict gel properties and guide composition/structure selection. In analytics, physics-informed models are enhancing impedance and voltammetry interpretation and reliability. Building on these trends, this review outlines standards for dataset curation (metadata on ionic milieu, temperature, humidity history, and mechanical loading) and strategies for cross-user and domain generalization. This review closes with actionable design guidelines for standardization, real-time analytics, and the clinical translation of hydrogel wearables.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

Datasets cited

• doi:10.17632/c3rxvdgbrs.1 — at the source; found in the references
• doi:10.17632/cx8cfgrhnn.1 — at the source; found in the references

Data Availability Statement

No new data were created or analyzed in this study. Data sharing is not applicable to this article.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 1 author, 8 keywords, 1 funder, 210 references.

Cite

This paper

Zhang, Z. (2026). Machine-Learning-Enabled Hydrogel Biosensors for Wearable Health Monitoring. Gels (Basel, Switzerland), 12(5), 449. https://doi.org/10.3390/gels12050449

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