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Managing stress is essential for mental and physical health, yet current methods rely on subjective self-assessments or indirect physiological measurements, often lacking accuracy. Existing wearable sensors primarily target a single stress hormone, cortisol, using single-point measurements that fail to capture real-time changes and distinguish between acute and chronic stress. To address this, we present Stressomic, a wearable multiplexed microfluidic biosensor for noninvasive monitoring of cortisol, epinephrine, and norepinephrine in sweat. Stressomic integrates iontophoresis-driven sweat extraction with bursting valve-regulated microfluidic channels for continuous sampling and analysis. Gold nanodendrite–decorated laser-engraved graphene electrodes achieve picomolar-level sensitivity, enabling simultaneous detection of multiple stress hormones. Electrochemical assays and human studies demonstrate that Stressomic reliably tracks hormone fluctuations in response to physical, psychological, and pharmacological stressors. Distinct temporal patterns reveal the dynamic interplay between the hypothalamic-pituitary-adrenal axis and the sympathetic nervous system. This platform enables continuous, multiplexed stress profiling, offering opportunities for early detection of maladaptive responses, personalized stress management, and deeper insights into stress biology. 

Abstract Recent advancements in wearable sensor technologies have enabled real-time monitoring of physiological and biochemical signals, opening new opportunities for personalized healthcare applications. However, conventional wearable devices often depend on rigid electronics components for signal transduction, processing, and wireless communications, leading to compromised signal quality due to the mechanical mismatches with the soft, flexible nature of human skin. Additionally, current computing technologies face substantial challenges in efficiently processing these vast datasets, with limitations in scalability, high power consumption, and a heavy reliance on external internet resources, which also poses security risks. To address these challenges, we have developed a miniaturized, standalone, chip-less wearable neuromorphic system capable of simultaneously monitoring, processing, and analyzing multimodal physicochemical biomarker data (i.e., metabolites, cardiac activities, and core body temperature). By leveraging scalable printing technology, we fabricated artificial synapses that function as both sensors and analog processing units, integrating them alongside printed synaptic nodes into a compact wearable system embedded with a medical diagnostic algorithm for multimodal data processing and decision making. The feasibility of this flexible wearable neuromorphic system was demonstrated in sepsis diagnosis and patient data classification, highlighting the potential of this wearable technology for real-time medical diagnostics.