Evidence map›Paper›PMID 40593594›Full record

ArticleNature communications2025

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 and 2 more

Abstract read
In one paragraph

Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

26 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Article
  5. Review
  6. Neuromorphic Devices and Computing for Sensing, Memory, and Control.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  7. Article
  8. Article
  9. Article
  10. Review
  11. Photonic-Enabled Energy-Efficient Transparent Neuromorphic Computing Devices: A Review.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  12. Electrochemical in-biosensing computing.National science review · 2026
    Article
  13. Review
  14. Review
  15. Multimodal Wearable Biosensing Meets Multidomain AI: A Pathway to Decentralized Healthcare.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  16. Review
  17. Article
  18. Article
  19. Ionic-Bionic Interfaces: Advancing Iontronic Strategies for Bioelectronic Sensing and Therapy.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026
    Review
  20. Nanomaterial-Based Inkjet Printing for Electrochemical Sensing.Small (Weinheim an der Bergstrasse, Germany) · 2026
    Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yongsuk Choi *Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.
Peng Jin *Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.ORCID http://orcid.org/0000-0002-4952-2606
Sanghyun Lee *Andrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.
Yu SongAndrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.ORCID http://orcid.org/0000-0002-4185-2256
Roland Yingjie TayAndrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.
Gwangmook KimAndrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.ORCID http://orcid.org/0000-0002-7469-408X
Jounghyun YooAndrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.ORCID http://orcid.org/0000-0003-4253-2382
Hong HanAndrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.
Jeonghee YeomAndrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA.ORCID http://orcid.org/0000-0001-6320-034X
Jeong Ho ChoDepartment of Chemical and Biomolecular Engineering, Yonsei University, Seoul, Republic of Korea. jhcho94@yonsei.ac.kr.ORCID http://orcid.org/0000-0002-1030-9920
Dong-Hwan KimDepartment of Chemical Engineering, Sungkyunkwan University, Suwon, Republic of Korea. dhkim1@skku.edu.ORCID http://orcid.org/0000-0002-2753-0955
Wei GaoAndrew and Peggy Cherng Department of Medical Engineering, California Institute of Technology, Pasadena, CA, USA. weigao@caltech.edu.ORCID http://orcid.org/0000-0002-8503-4562

Funding

Laser-Engraved Wearable Sweat Sensors to Detect and Monitor Cardiometabolic DiseaseR01HL155815 · NHLBI · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI GAO, WEI · 2021 to 2025
$2.0M
Develop a wireless, skin-conformable and dual-sensing wearable system in support of voice and upper airway telehealth careR01DC021461 · NIDCD · CALIFORNIA INSTITUTE OF TECHNOLOGY · PI Wei Gao, Jianyu Li · 2024 to 2026
$1.8M
National Research Foundation of Korea (NRF) 2020R1A5A1018052National Science Foundation (NSF) 2444815NHLBI NIH HHS R01 HL155815NIDCD NIH HHS R01 DC021461United States Department of Defense | United States Army | Army Medical Command | Medical Research and Materiel Command (U.S. Army Medical Research and Materiel Command) HT9425-24-1-0249United States Department of Defense | United States Army | U.S. Army Research, Development and Engineering Command | Army Research Office (ARO) W911NF-23-1-0041U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01DC021461U.S. Department of Health & Human Services | National Institutes of Health (NIH) R01HL155815
6 · The paper itself

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.

Indexed as

Wearable Electronic DevicesAlgorithmsHumansMonitoring, PhysiologicNeural Networks, ComputerSepsisSignal Processing, Computer-Assisted

Identifiers

PMID40593594
PMCPMC12216833

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.