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

Yongsuk Choi, Peng Jin, Sanghyun Lee, Yu Song, Roland Yingjie Tay, Gwangmook Kim, Jounghyun Yoo, Hong Han, Jeonghee Yeom, Jeong Ho Cho (), Dong-Hwan Kim () and Wei Gao ()
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Yongsuk Choi: California Institute of Technology
Peng Jin: California Institute of Technology
Sanghyun Lee: California Institute of Technology
Yu Song: California Institute of Technology
Roland Yingjie Tay: California Institute of Technology
Gwangmook Kim: California Institute of Technology
Jounghyun Yoo: California Institute of Technology
Hong Han: California Institute of Technology
Jeonghee Yeom: California Institute of Technology
Jeong Ho Cho: Yonsei University
Dong-Hwan Kim: Sungkyunkwan University
Wei Gao: California Institute of Technology

Nature Communications, 2025, vol. 16, issue 1, 1-11

Abstract: 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.

Date: 2025
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DOI: 10.1038/s41467-025-60854-7

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