Evidence map›Paper›PMID 42127197›Full record

ArticleScience advances2026

Wireless, skin-interfaced multimodal sensing system for continuous psychophysiological monitoring-A wearable polygraph device.

Sun Hong Kim, Tae Wan Park, Seunghee Cho, Tianyu Yang, Seonggwang Yoo, Khaytin Ilya, Jacie R McHaney, Jana Jaffe, Anisha Kshetrapal, Yue Wang and 12 more

Erratum issuedAbstract read
In one paragraph

Article in Science advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

22 authors.

Sun Hong KimQuerrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA.ORCID 0000-0003-1212-3537
Tae Wan ParkQuerrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA.ORCID 0000-0003-1137-5273
Seunghee ChoQuerrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA.
Tianyu YangQuerrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA.ORCID 0000-0003-2109-3413
Seonggwang YooSchool of Glocal Leaders, Glocal Honors College, Inje University, Gimhae 50834, Republic of Korea.ORCID 0000-0002-9126-927X
Khaytin IlyaDivision of Autonomic Medicine, Department of Paediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL 60611, USA.ORCID 0000-0001-8560-7730
Jacie R McHaneyRoxelyn and Richard Pepper Department of Communication Sciences and Disorders, Northwestern University, Evanston, IL 60208, USA.ORCID 0000-0002-1148-4018
Jana JaffeDepartment of Pediatrics, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA.ORCID 0000-0002-2404-162X
Anisha KshetrapalDepartment of Pediatrics, Northwestern University, Feinberg School of Medicine, Chicago, IL 60611, USA.ORCID 0000-0001-8092-4681
Yue WangQuerrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA.ORCID 0000-0002-9763-3570
Yunyun WuQuerrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA.ORCID 0000-0002-1536-1488
Jan-Kai ChangQuerrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA.ORCID 0000-0002-3056-1250
Jihun ParkQuerrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA.ORCID 0000-0002-4402-6881
Hak-Young AhnQuerrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA.ORCID 0000-0002-5441-1054
Min-Seung JoQuerrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA.ORCID 0000-0003-4486-7725
Jacob TruebQuerrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA.ORCID 0000-0002-1627-5178
Yei Hwan JungDepartment of Electronic Engineering, Hanyang University, Seoul, Republic of Korea.ORCID 0000-0002-1754-2602
Seyong OhDivision of Electrical Engineering, Hanyang University ERICA, Ansan, Republic of Korea.ORCID 0000-0002-3989-1117
Sang Min WonDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea.ORCID 0000-0002-5750-8628
Debra E Weese-MayerDivision of Autonomic Medicine, Department of Paediatrics, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL 60611, USA.ORCID 0000-0001-6061-220X
Jae-Young YooDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Republic of Korea.ORCID 0000-0003-0934-4718
John A RogersQuerrey Simpson Institute for Bioelectronics, Northwestern University, Evanston, IL 60208, USA.ORCID 0000-0002-3830-5980

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Accurate, continuous monitoring of psychophysiological states is central to understanding stress and autonomic dysfunction across diverse medical contexts. Current approaches such as polygraphy and polysomnography rely on cumbersome, wired sensors that limit real-world utility and burden patients, particularly vulnerable populations such as infants. Here, we introduce a wireless, skin-interfaced multimodal sensing system capable of simultaneously recording cardiac, respiratory, electrodermal, and thermal signals in a time-synchronized manner. Leveraging compact and soft designs, the technology enables unobtrusive monitoring across controlled, clinical, and naturalistic settings. Validation studies performed in parallel with gold standard systems demonstrate high fidelity in quantifying stress responses during polygraph interviews, cognitive load tasks, and cold pressor tests. In pediatric sleep studies, the data reliably identify arousals, hypopnea, and apnea while revealing disease-specific autonomic signatures in infants with Down syndrome. Real-world deployment during emergency simulation training shows that multimodal stress signatures correlate inversely with performance, underscoring translational value in medical education. Machine learning analyses across all studies confirm that multimodal features outperform single-signal approaches in detecting stress and clinical events with high sensitivity and specificity. Collectively, these findings establish the technology as a next-generation wearable platform that bridges engineering innovation and clinical practice, offering mechanistic insight and diagnostic potential in stress medicine, sleep medicine, and beyond.

Indexed as

PolysomnographyWearable Electronic DevicesWireless TechnologyHumansMonitoring, Physiologic

Identifiers

PMID42127197
PMCPMC13170674

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.