ArticleAdvanced sensor research2025
Wearable Single-Electrode Capacitive Sensor with Large Penetration Depth for Intelligent Deep Tissue and Hemorrhage Monitoring.
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.
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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.
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Who cites it
2 citing papers in PubMed.
- Continuous blood pressure monitoring via hemodynamic parameter and pulse transit time derived from capacitive sensing pads.Physiological measurement · 2026Article
- Non-Invasive Multidimensional Capacitive Sensing for In Vivo Traumatic Brain Injury Monitoring.Advanced materials technologies · 2026Article
Corrections and comments
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Authors and funding
8 authors.
Funding
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.
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Registered trials
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