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

Other Titles
Wireless, skin-interfaced multimodal sensing system for continuous psychophysiological monitoring—A wearable polygraph device
Authors
Kim, Sun HongPark, Tae WanCho, SeungheeYang, TianyuYoo, SeonggwangIlya, KhaytinMcHaney, Jacie R.Jaffe, JanaKshetrapal, AnishaWang, YueWu, YunyunChang, Jan-KaiPark, JihunAhn, Hak-YoungJo, Min-SeungTrueb, JacobJung, Yei HwanOh, SeyongWon, Sang MinWeese-Mayer, Debra E.Yoo, Jae-YoungRogers, John A.
Issue Date
May-2026
Publisher
AMER ASSOC ADVANCEMENT SCIENCE
Citation
SCIENCE ADVANCES, v.12, no.20, pp 1 - 15
Pages
15
Indexed
SCIE
SCOPUS
Journal Title
SCIENCE ADVANCES
Volume
12
Number
20
Start Page
1
End Page
15
URI
https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/212892
DOI
10.1126/sciadv.aed3162
ISSN
2375-2548
2375-2548
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.
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ERICA 공학대학 (SCHOOL OF ELECTRICAL ENGINEERING)
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