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Flow-Field Inference for Turbulent Exhale Flow Measurement

Authors :
Shane Transue
Do-kyeong Lee
Jae-Sung Choi
Seongjun Choi
Min Hong
Min-Hyung Choi
Source :
Diagnostics, Vol 14, Iss 15, p 1596 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Background: Vision-based pulmonary diagnostics present a unique approach for tracking and measuring natural breathing behaviors through remote imaging. While many existing methods correlate chest and diaphragm movements to respiratory behavior, we look at how the direct visualization of thermal CO2 exhale flow patterns can be tracked to directly measure expiratory flow. Methods: In this work, we present a novel method for isolating and extracting turbulent exhale flow signals from thermal image sequences through flow-field prediction and optical flow measurement. The objective of this work is to introduce a respiratory diagnostic tool that can be used to capture and quantify natural breathing, to identify and measure respiratory metrics such as breathing rate, flow, and volume. One of the primary contributions of this work is a method for capturing and measuring natural exhale behaviors that describe individualized pulmonary traits. By monitoring subtle individualized respiratory traits, we can perform secondary analysis to identify unique personalized signatures and abnormalities to gain insight into pulmonary function. In our study, we perform data acquisition within a clinical setting to train an inference model (FieldNet) that predicts flow-fields to quantify observed exhale behaviors over time. Results: Expiratory flow measurements capturing individualized flow signatures from our initial cohort demonstrate how the proposed flow field model can be used to isolate and analyze turbulent exhale behaviors and measure anomalous behavior. Conclusions: Our results illustrate that detailed spatial flow analysis can contribute to unique signatures for identifying patient specific natural breathing behaviors and abnormality detection. This provides the first-step towards a non-contact respiratory technology that directly captures effort-independent behaviors based on the direct measurement of imaged CO2 exhaled airflow patterns.

Details

Language :
English
ISSN :
14151596 and 20754418
Volume :
14
Issue :
15
Database :
Directory of Open Access Journals
Journal :
Diagnostics
Publication Type :
Academic Journal
Accession number :
edsdoj.48970309c5c4e5385ff0c2cb3fed0d0
Document Type :
article
Full Text :
https://doi.org/10.3390/diagnostics14151596