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Review
. 2024 Dec 19;24(24):8118.
doi: 10.3390/s24248118.

Thermal Cameras for Continuous and Contactless Respiration Monitoring

Affiliations
Review

Thermal Cameras for Continuous and Contactless Respiration Monitoring

Raquel Alves et al. Sensors (Basel). .

Abstract

Continuous respiration monitoring is an important tool in assessing the patient's health and diagnosing pulmonary, cardiovascular, and sleep-related breathing disorders. Various techniques and devices, both contact and contactless, can be used to monitor respiration. Each of these techniques can provide different types of information with varying accuracy. Thermal cameras have become a focal point in research due to their contactless nature, affordability, and the type of data they provide, i.e., information on respiration motion and respiration flow. Several studies have demonstrated the feasibility of this technology and developed robust algorithms to extract important information from thermal camera videos. This paper describes the current state-of-the-art in respiration monitoring using thermal cameras, dividing the system into acquiring data, defining and tracking the region of interest, and extracting the breathing signal and respiration rate. The approaches taken to address the various challenges, the limitations of these methods, and possible applications are discussed.

Keywords: remote thermography; respiration monitoring; respiration rate; respiratory flow; thermal cameras.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Non-contact respiration monitoring techniques divided into the respiration rate obtained through flow data and respiration rate obtained through motion data.
Figure 2
Figure 2
Pipeline for respiration rate monitoring and apnea detection in acquisitions with thermal cameras. Each step in the pipeline is described in the section indicated in parentheses.
Figure 3
Figure 3
Recreation of a thermal image acquired by Murthy et al. in their 2009 study [40] and respective ROI computed.
Figure 4
Figure 4
(left) Example of an acquired thermal image (from three cameras); (middle) matrix combining all the extracted features; (right) ROI selected pixels [62].

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