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使用移动热成像相机和人工智能回归技术的非接触式肺活量测定法。

Non-Contact Spirometry Using a Mobile Thermal Camera and AI Regression.

作者信息

Fraiwan Luay, Khasawneh Natheer, Lweesy Khaldon, Elbalki Mennatalla, Almarzooqi Amna, Abu Hamra Nada

机构信息

Department of Electrical, Computer and Biomedical Engineering, Abu Dhabi University, Abu Dhabi 55991, United Arab Emirates.

Department of Biomedical Engineering, Jordan University of Science and Technology, Irbid 2210, Jordan.

出版信息

Sensors (Basel). 2021 Nov 15;21(22):7574. doi: 10.3390/s21227574.

DOI:10.3390/s21227574
PMID:34833650
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8624693/
Abstract

Non-contact physiological measurements have been under investigation for many years, and among these measurements is non-contact spirometry, which could provide acute and chronic pulmonary disease monitoring and diagnosis. This work presents a feasibility study for non-contact spirometry measurements using a mobile thermal imaging system. Thermal images were acquired from 19 subjects for measuring the respiration rate and the volume of inhaled and exhaled air. A mobile application was built to measure the respiration rate and export the respiration signal to a personal computer. The mobile application acquired thermal video images at a rate of nine frames/second and the OpenCV library was used for localization of the area of interest (nose and mouth). Artificial intelligence regressors were used to predict the inhalation and exhalation air volume. Several regressors were tested and four of them showed excellent performance: random forest, adaptive boosting, gradient boosting, and decision trees. The latter showed the best regression results, with an R-square value of 0.9998 and a mean square error of 0.0023. The results of this study showed that non-contact spirometry based on a thermal imaging system is feasible and provides all the basic measurements that the conventional spirometers support.

摘要

非接触式生理测量已经研究多年，其中非接触式肺活量测定法能够对急慢性肺部疾病进行监测和诊断。本文介绍了一项使用移动热成像系统进行非接触式肺活量测定的可行性研究。从19名受试者获取热图像，以测量呼吸频率以及吸入和呼出空气的体积。构建了一个移动应用程序来测量呼吸频率并将呼吸信号导出到个人计算机。该移动应用程序以每秒9帧的速率获取热视频图像，并使用OpenCV库对感兴趣区域（鼻子和嘴巴）进行定位。使用人工智能回归器预测吸入和呼出的空气体积。测试了多个回归器，其中四个表现出色：随机森林、自适应增强、梯度增强和决策树。后者显示出最佳的回归结果，决定系数R²值为0.9998，均方误差为0.0023。本研究结果表明，基于热成像系统的非接触式肺活量测定是可行的，并且能够提供传统肺活量计所支持的所有基本测量。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/2ac336341d8e/sensors-21-07574-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/2523de67f024/sensors-21-07574-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/9c142ee3e40e/sensors-21-07574-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/d02493eff1b0/sensors-21-07574-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/64ed6d5c755c/sensors-21-07574-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/d6ddb2ddaafc/sensors-21-07574-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/55ab4e3ab4f9/sensors-21-07574-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/3817cafbd0d0/sensors-21-07574-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/ab7bbaaeea06/sensors-21-07574-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/7a1ff8829a4b/sensors-21-07574-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/2ac336341d8e/sensors-21-07574-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/2523de67f024/sensors-21-07574-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/6e20e6f432fa/sensors-21-07574-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/80363ead674c/sensors-21-07574-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/af968e16582a/sensors-21-07574-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/9c142ee3e40e/sensors-21-07574-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/d02493eff1b0/sensors-21-07574-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/64ed6d5c755c/sensors-21-07574-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/d6ddb2ddaafc/sensors-21-07574-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/55ab4e3ab4f9/sensors-21-07574-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/3817cafbd0d0/sensors-21-07574-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/ab7bbaaeea06/sensors-21-07574-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/7a1ff8829a4b/sensors-21-07574-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/8970/8624693/2ac336341d8e/sensors-21-07574-g013.jpg

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