Dynamic-Covalent Hydrogel Sensors for Machine-Learning-Assisted Parkinsonian Assessment

Abstract

Wearable Hydrogel Biosensing depends on a defensible relationship between performance with evidence quality, resource limits, and transfer across settings. The present synthesis investigates connecting material mechanics, multimodal sensing, model interpretation, and secure interaction. The review triangulates 1 focal paper with 11 independently retrieved publications confirmed at bibliographic registration or publisher level. The analysis is organized around network chemistry, mechanical compliance, signal stability, clinical features, and privacy. Rather than treating metrics from unlike protocols as commensurate, the review compares units of analysis, methodological commitments, and evidence limits. Across the literature, a robust inference is that advances in wearable hydrogel biosensing become credible when measurement, model, and clinical decision are evaluated together and when uncertainty about calibration is reported explicitly. The resulting account aligns method selection to downstream risk and the conditions that weaken external validity, and proposes a research agenda centered on transparent baselines, stress testing, and reproducible evidence.