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Behavioral analysis of turbulent exhale flows | IEEE Conference Publication | IEEE Xplore

Behavioral analysis of turbulent exhale flows


Abstract:

Dense exhale flow through CO2 spectral imaging introduces a pivotal trajectory within non-contact respiratory analysis that consolidates several pulmonary evaluations int...Show More

Abstract:

Dense exhale flow through CO2 spectral imaging introduces a pivotal trajectory within non-contact respiratory analysis that consolidates several pulmonary evaluations into a single coherent monitoring process. Due to technical limitations and the limited exploration of respiratory analysis through this non-contact technique, this method has not been fully utilized to extract high-level respiratory behaviors through turbulent exhale analysis. In this work, we present a structural foundation for respiratory analysis of turbulent exhale flows through the visualization of dense CO2 density distributions using precisely refined thermal imaging device to target high-resolution respiratory modeling. We achieve spatial and temporal high-resolution flow reconstructions through the cooperative development of a thermal camera dedicated to respiratory analysis to drastically improve the precision of current exhale imaging methods. We then model turbulent exhale behaviors using a heuristic volumetric flow reconstruction process to generate sparse flow exhale models. Together these contributions allow us to target the acquisition of numerous respiratory behaviors including, breathing rate, exhale strength and capacity, towards insights into lung functionality and tidal volume estimation.
Date of Conference: 04-07 March 2018
Date Added to IEEE Xplore: 09 April 2018
ISBN Information:
Conference Location: Las Vegas, NV, USA
Citations are not available for this document.

I. Introduction

Accurate non-contact respiratory analysis has recently gained popularity within the domains of wireless signal processing [8] and computer vision [12] to automate and significantly broaden the class of quantitative respiratory metrics that non-contact methods can reliably address. Numerous techniques exist for both contact and non-contact respiratory analysis [4], however all of these methods indirectly infer breathing behaviors or utilize correlation functions for respiratory analysis. Techniques within computer vision have introduced thermal infrared cameras with spectral filters for CO2imaging for respiratory analysis [2], however the applicability of these techniques to comprehensive respiratory analysis is severely underdeveloped and the adoption of these methods has been very limited. This is due to three primary factors shared between most prior vision-based techniques: (1) prior objectives only emphasize simple quantitative measures such as respiratory rate [3] within limited Regions of Interest (RoI) and strength [6], limiting potential high-level behavioral analysis, (2) prior devices lack the sensitivity required to monitor subtle density variances and complex flows behaviors for identifying respiratory conditions, and (3) frame-rate limitations inhibit the ability to accurately capture rapid and turbulent respiratory behaviors. To develop a device for directly analyzing turbulent exhale flows [3], we have coordinated the development of a hyper-sensitive FLIR thermal camera that contains an embedded spectral filter that directly targets the spectral band . From our requirement specification

FLIR A6788sc InSb CCF, 640 × 512 resolution count images @ 30-120[fps] with programmatic camera control and raw data acquisition.

, the device provides raw count images that contain the infrared wavelength activation counts within the absorption band [10], [11]. Through the development these imaging methods and our direct measurements of breathing behavior, we introduce a new vector in vision-based clinical respiratory analysis. This includes direct flow and thermal analysis for subtle alternations in airflow related to asthma, Chronic Obstructive Pulmonary Disease (COPD), developmental conditions related to nose and mouth breathing distributions, cognitive function [13], sleep apnea, and Sudden Infant Death Syndrome (SIDS).

Dense exhale flow analysis through optimized imaging for illustrating unique respiratory behaviors of multiple individuals (a-b).

Cites in Papers - |

Cites in Papers - IEEE (2)

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1.
Shane Transue, Se Dong Min, Min-Hyung Choi, "Expiratory Flow and Volume Estimation Through Thermal-CO {}_{\text{2}} Imaging", IEEE Transactions on Biomedical Engineering, vol.70, no.7, pp.2111-2121, 2023.
2.
Breawn Schoun, Shane Transue, Min-Hyung Choi, "Non-contact Medium-based Respiratory Analysis through Reinforced Hybrid Model", 2019 IEEE SENSORS, pp.1-4, 2019.

Cites in Papers - Other Publishers (1)

1.
Sayed Mohsin Reza, Md Al Masum Bhuiyan, Nishat Tasnim, "A convolution neural network with encoder-decoder applied to the study of Bengali letters classification", Big Data and Information Analytics, vol.6, no.0, pp.41, 2021.
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References

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