Evidence map›Paper›PMID 40837055›Full record

ArticleAdvanced sensor research2025

Wearable Single-Electrode Capacitive Sensor with Large Penetration Depth for Intelligent Deep Tissue and Hemorrhage Monitoring.

Yu-Jen Cheng, Shawn Kim, Nathan White, Xu Wang, Kristyn Ringgold, Lauren Neidig, Younghoon Kwon, Jae-Hyun Chung

Abstract read
In one paragraph

Article in Advanced sensor research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

8 authors.

Yu-Jen ChengDepartment of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.
Shawn KimDepartment of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.
Nathan WhiteDepartment of Emergency Medicine, Resuscitation Engineering Science Unit, University of Washington, Seattle, WA 98195 USA.
Xu WangDepartment of Emergency Medicine, Resuscitation Engineering Science Unit, University of Washington, Seattle, WA 98195 USA.
Kristyn RinggoldDepartment of Emergency Medicine, Resuscitation Engineering Science Unit, University of Washington, Seattle, WA 98195 USA.
Lauren NeidigDepartment of Comparative Medicine, University of Washington, Seattle, WA 98195 USA.
Younghoon KwonDivision of Cardiology, University of Washington, Seattle, WA 98195 USA.
Jae-Hyun ChungDepartment of Mechanical Engineering, University of Washington, Seattle, WA 98195, USA.

Funding

Sleep Apnea-Specific Nocturnal Blood Pressure Surge to Determine Cardiovascular Risks and Therapeutic Benefits in Patients with Obstructive Sleep ApneaR01HL158765 · NHLBI · UNIVERSITY OF WASHINGTON · PI KWON, YOUNGHOON · 2021 to 2025
$3.5M
Photoplethysmography Analysis to Assess Cardio-Cerebrovascular Impact of SleepR21AG070576 · NIA · UNIVERSITY OF WASHINGTON · PI KWON, YOUNGHOON · 2020 to 2021
$530k
Deep learning of awake and sleep electrocardiography to identify atrial fibrillation risk in sleep apneaR21HL167126 · NHLBI · UNIVERSITY OF WASHINGTON · PI AKBILGIC, OGUZ, AZARBARZIN, ALI · 2023 to 2024
$202k
NHLBI NIH HHS R01 HL158765NHLBI NIH HHS R21 HL167126NIA NIH HHS R21 AG070576
6 · The paper itself

Abstract

Monitoring deep tissue biometrics is crucial in various clinical settings, including internal hemorrhage. Although optical and impedance tomography techniques offer real-time monitoring with minimal medical infrastructure, they still face challenges in accurately assessing deeper tissues in wearable formats. This study introduces a novel single-electrode capacitive sensor designed to measure deep tissue capacitance changes caused by variations in dielectric constant and pressure. The sensor features a carbon nanotube-paper composite (CPC) electrode integrated with a multi-walled carbon nanotube (MWCNT)-embedded foam. The CPC electrode, with its large surface area and high-aspect-ratio fibers, generates a high electric field for deeper tissue penetration, improving deep tissue monitoring performance. Penetration depth is characterized using surrogate tissue, heart, and lung models. Additionally, the integration of pressure-sensitive MWCNT foam significantly enhances the sensitivity, enabling precise detection of regional blood volume and tissue displacement. The novel sensing mechanism is applied to detect internal hemorrhage in a porcine model. By employing a machine learning algorithm, the sensor accurately estimates the severity of internal hemorrhage, offering a noninvasive alternative to catheter-based systems. This advancement lays the foundation for a real-time wearable system that monitors deep tissue health metrics, such as blood volume, blood pressure, and lung function.

Indexed as

deep tissue biometricshemorrhagemachine learning algorithmnanocompositessingle-electrode capacitive sensor

Identifiers

PMID40837055
PMCPMC12363426

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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.