Switching on Smart Care: The Ascendancy of Wireless Technologies in Continuous Health Surveillance

Abstract

Guided by such relentless scientific curiosity, the field of wearable diagnostics has evolved from experimental concepts into sophisticated, organ-centric platforms capable of capturing rich physiological and biochemical data in real time. This review encapsulates the interdisciplinary transformation wherein bioelectronics, materials science, and artificial intelligence (AI) converge to create next-generation wearables that intimately interface with organs such as the brain, eyes, heart, skin, and lungs. Graphene-based imperceptible e-skins now enable neuromuscular signal acquisition with angular resolutions approaching ∼4° and signal fidelity exceeding traditional Ag/AgCl electrodes. AI-enhanced electroencephalographic (EEG) headbands decode motor intent with >92% accuracy in under 2 s, paving the way for real-time brain–computer interactions. Simultaneously, noninvasive microneedle arrays and sweat-interfacing chemosensors demonstrate femtomolar sensitivity for glucose, lactate, and even nucleic acids, boasting >80% correlation with gold-standard clinical assays. The domain has experienced a >60% increase in advanced functional materials-PEDOT: PSS hybrids, MXenes, oxide nanosheets-and a >70% rise in mechanical adaptability and miniaturization, dramatically expanding diagnostic possibilities in ambulatory environments. Dry electrode systems in smart eyewear, epidermal patches, and Virtual Reality (VR)-integrated systems now maintain <1.13 μV Root Mean Square (RMS) noise levels, 98–99% classification accuracy, and uninterrupted operation exceeding 12 hours, even in motion-rich conditions. As these intelligent, autonomous devices continue to shrink the gap between biological and digital systems, they are poised not merely to monitor health but to redefine human–machine symbiosis in the era of predictive and personalized medicine.

Keywords

wearable biosensors / smart health monitoring / dry electrode systems / artificial intelligence in healthcare / organ-centric diagnostics

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