All-printed chip-less wearable neuromorphic system for multimodal physicochemical health monitoring

• Published in: Nature communications Vol. 16; no. 1; pp. 5689 - 11
• Main Authors: Choi, Yongsuk, Jin, Peng, Lee, Sanghyun, Song, Yu, Tay, Roland Yingjie, Kim, Gwangmook, Yoo, Jounghyun, Han, Hong, Yeom, Jeonghee, Cho, Jeong Ho, Kim, Dong-Hwan, Gao, Wei
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 01.07.2025; Nature Publishing Group; Nature Portfolio
• Subjects: 639/166/985; 639/166/987; 639/301/1005; Algorithms; Biomarkers; Biosensors; Body temperature; Data collection; Data processing; Decision making; Electrodes; Glucose; Heart rate; Humanities and Social Sciences; Humans; Internet resources; Medical electronics; Metabolites; Microprocessors; Monitoring, Physiologic - instrumentation; Monitoring, Physiologic - methods; multidisciplinary; Nanoparticles; Neural Networks, Computer; Physiology; Plasma etching; Power consumption; Real time; Scanning electron microscopy; Science; Science (multidisciplinary); Sensors; Sepsis; Sepsis - diagnosis; Signal processing; Signal Processing, Computer-Assisted; Signal quality; Signal transduction; Skin; Synapses; Telemedicine; Transistors; Uric acid; Wearable Electronic Devices; Wearable technology; Wireless communications
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-025-60854-7

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. The authors present a chip-less wearable sensor-processor integrated neuromorphic system that combines multimodal physicochemical sensing with neuromorphic processing for real-time, autonomous physiological monitoring.