Evidence map›Paper›PMID 40173233›Full record

ArticleScience advances2025

Starfish-inspired wearable bioelectronic systems for physiological signal monitoring during motion and real-time heart disease diagnosis.

Sicheng Chen, Qunle Ouyang, Xianglin Meng, Yibo Yang, Can Li, Xuanbo Miao, Zehua Chen, Ganggang Zhao, Yaguo Lei, Bernard Ghanem and 3 more

Abstract read
In one paragraph

Article in Science advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing papers in PubMed
–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

12 citing papers in PubMed.

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  10. The future of the sleep field using large language models in mental health care.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025
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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

13 authors.

Sicheng ChenDepartment of Chemical and Biomedical Engineering, University of Missouri, Columbia, MO, USA.ORCID 0000-0001-5613-5105
Qunle OuyangDepartment of Mechanical and Aerospace Engineering, University of Missouri, Columbia, MO, USA.ORCID 0009-0000-8547-4210
Xianglin MengDepartment of Critical Care Medicine, The First Affiliated Hospital of Harbin Medical University, Harbin, China.ORCID 0000-0001-5091-3260
Yibo YangKing Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia.ORCID 0000-0003-0530-7231
Can LiDoorDash Inc., San Francisco, CA, USA.
Xuanbo MiaoDepartment of Mechanical and Aerospace Engineering, University of Missouri, Columbia, MO, USA.ORCID 0000-0002-0667-0852
Zehua ChenDepartment of Mechanical and Aerospace Engineering, University of Missouri, Columbia, MO, USA.ORCID 0009-0007-4138-584X
Ganggang ZhaoDepartment of Mechanical and Aerospace Engineering, University of Missouri, Columbia, MO, USA.ORCID 0000-0002-6363-5295
Yaguo LeiMechanical Engineering College, Xi'an Jiaotong University, Xi'an, Shaanxi, China.ORCID 0000-0002-5167-1459
Bernard GhanemKing Abdullah University of Science and Technology, Thuwal, Kingdom of Saudi Arabia.ORCID 0000-0002-5534-587X
Sandeep GautamDivision of Cardiovascular Medicine, University of Missouri, Columbia, MO, USA.ORCID 0000-0002-5629-0145
Jianlin ChengDepartment of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO, USA.ORCID 0000-0003-0305-2853
Zheng YanDepartment of Chemical and Biomedical Engineering, University of Missouri, Columbia, MO, USA.ORCID 0000-0001-5968-0934

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Soft bioelectronics enable noninvasive, continuous monitoring of physiological signals, essential for precision health care. However, capturing biosignals during physical activity, particularly biomechanical signals like cardiac mechanics, remains challenging due to motion-induced interference. Inspired by starfish's pentaradial symmetry, we introduce a starfish-like wearable bioelectronic system designed for high-fidelity signal monitoring during movement. The device, featuring five flexible, free-standing sensing arms connected to a central electronic hub, substantially reduces mechanical interference and enables high-fidelity acquisition of cardiac electrical (electrocardiogram) and mechanical (seismocardiogram and gyrocardiogram) signals during motion when coupled with signal compensation and machine learning. Using these three cardiac signal types as inputs, machine learning models deployed on smart devices achieve real-time, high-accuracy (more than 91%) diagnoses of heart conditions such as atrial fibrillation, myocardial infarction, and heart failure. These findings open previously undiscovered avenues by leveraging bioinspired device concepts combined with cutting-edge data science to boost bioelectronic performance and diagnostic precision.

Indexed as

Heart DiseasesStarfishWearable Electronic DevicesAnimalsElectrocardiographyHumansMachine LearningMonitoring, PhysiologicMotionSignal Processing, Computer-Assisted

Identifiers

PMID40173233
PMCPMC11963991

What Socratic 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 Socratic graph.