Multimodal Wearable Biosensing and Edge AI for Personalized Health: A Comprehensive Review

Abstract

Wearable biosensing is moving beyond single-signal activity tracking toward multimodal, AI-assisted health monitoring that combines biophysical streams with biochemical information from sweat, interstitial fluid, tears, and other accessible biofluids. Recent work has accelerated progress in flexible optical materials, programmable DNA-based sensing architectures, biosafety-aware sweat patches, and edge AI pipelines capable of denoising, calibration, personalization, and low-latency inference. This review synthesizes current advances across general biosensor platforms, vital-sign monitoring, biochemical sweat sensing, motion and biomechanics sensing, and edge AI/data analytics. Particular attention is given to the translational bottlenecks that now dominate the field, including motion artifacts, sensor drift, biofouling, subject-to-subject variability, limited sweat-to-blood equivalence, insufficient external validation, and uneven regulatory readiness. The central argument of this updated review is that the next phase of progress will not be driven by sensitivity alone but by robust multimodal fusion, clinically anchored validation, interoperable data pipelines, and energy-efficient on-device intelligence. By linking materials, electronics, algorithms, and deployment constraints, the review identifies the wearable biosensing strategies most likely to progress from promising laboratory demonstrations to reliable personalized-health tools.