Time-Dependent FEM Modeling for Transcutaneous Oxygen Sensors

Mohammed Almatrood, Bige Deniz Unluturk

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Keywords: transcutaneous oxygen sensor, wearable sensor, fem modeling

Abstract:

Continuous, real-time respiratory monitoring is essential in managing chronic respiratory diseases, perioperative care, and critical care settings, where the rapid and accurate detection of hypoxia can significantly impact patients' outcomes.
Transcutaneous oxygen monitoring (PtcO₂) is an emerging wearable sensing technology that measures the partial pressure of oxygen diffusing through the skin which correlates with arterial oxygen partial pressure (PaO₂), the clinical gold standard. Despite its promise, the clinical adoption of PtcO₂ has been limited. Prior studies have primarily focused on direct comparisons between PaO₂ and PtcO₂, yielding mixed results regarding their agreement and limiting PtcO₂ to trend monitoring.
To address this gap, we propose a finite element modeling (FEM) framework to investigate the physiological relationship between PaO₂ and PtcO₂. By simulating oxygen transport across multiple skin layers, the model provides mechanistic insights into transcutaneous oxygen dynamics under varying physiological conditions, such as arterial oxygen fluctuations and localized blood flow occlusion. This personalized computational model aims to improve the accuracy and reliability of transcutaneous oxygen monitoring, enabling more effective continuous respiratory assessment.

Track: 2. Sensors and systems for digital health, wellness, and athletics

NominateReviewer: Bige Deniz Unluturk, [e-mail]

Submission Number: 163