This summer, Min Choi, professor of computer science and engineering, and computer science post-doc Shane Transue received a $250k NSF award for their research involving breathing diagnostics using thermal imaging titled, “Remote Respiratory Diagnostics through Visualization of Exhale Flow.” Please see below for the abstract.
The broader impact/commercial potential of this Partnerships for Innovation – Technology Translation (PFI-TT) project is to enable the accurate detection of abnormal respiratory behaviors and breathing disorders through visualized exhale flow behaviors. Early and accurate detection of declines in pulmonary function through respiratory monitoring plays a vital role in the mitigation of serious health consequences caused by pulmonary diseases. For millions of Americans, these complications include decreased cognitive function, increased cardiovascular risk, as well as permanent disabilities including irreversible lung damage. These health conditions can be caused by a wide variety of pulmonological complications resulting from: Sleep-Disordered Breathing (SDB), Ear-Nose-Throat (ENT)-related conditions, and early-stage lung damage due to Chronic Obstructed Pulmonary Disease (COPD). The objective of this innovation is to broaden our understanding of natural, unconstrained breathing behaviors that can be used to identify abnormalities due to numerous pulmonary conditions and diseases. In this project, we propose to develop the first optical-based breathing diagnostic tool for pulmonologists that enables new diagnostic pulmonary evaluations. Through analysis of visualized flow behaviors, this technology enables a new form of non-contact analysis that can be used to: increase addressable patient populations including children and infants, improve diagnostic outcomes, and promote preventative care solutions for pulmonologists.
The proposed project incorporates the development, design, implementation, and validation of an integrated hardware-software diagnostic prototype facilitating visual, quantitative, and qualitative analysis of expiratory breathing behaviors. The underlying technology provides a unique approach to extracting physical measurements from visual data through the use of filtered thermal and depth imaging combined with data-driven fluid flow models. This approach includes a multifaceted design that integrates analytical, numerical, and machine learning methods to provide an inverse modeling approach for measuring respiratory behaviors. Key contributions of this research include: (1) accurate evaluation of pulmonary function based on visualized exhale behaviors providing quantitative measurements for flow, volume, exhale strength, and natural nose-mouth breathing distributions and (2) the development of a functional prototype that provides real-time visualization of expiratory breathing behaviors, continuous monitoring, and diagnostics. The realization of this innovation allows pulmonologists to break away from the current set of tube-based and wearable devices that require effort-based tests that are inconsistent, tedious, and uncomfortable for use in any extended duration. By introducing this new direction in how we monitor, measure, and evaluate pulmonary function, the proposed work strives to create a pivotal shift in the technological evaluation of natural breathing.
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