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