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Sensors 2017, 17(6), 1408; doi:10.3390/s17061408

Breathing Analysis Using Thermal and Depth Imaging Camera Video Records

1
Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague, Czech Republic
2
Faculty of Applied Informatics, Tomas Bata University in Zlín, 760 05 Zlín, Czech Republic
3
Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 166 36 Prague, Czech Republic
4
Faculty of Medicine in Hradec Králové, Department of Neurology, Charles University, 500 05 Hradec Kralove, Czech Republic
5
School of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK
*
Author to whom correspondence should be addressed.
Received: 8 April 2017 / Revised: 21 May 2017 / Accepted: 13 June 2017 / Published: 16 June 2017
(This article belongs to the Special Issue Imaging Depth Sensors—Sensors, Algorithms and Applications)
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Abstract

The paper is devoted to the study of facial region temperature changes using a simple thermal imaging camera and to the comparison of their time evolution with the pectoral area motion recorded by the MS Kinect depth sensor. The goal of this research is to propose the use of video records as alternative diagnostics of breathing disorders allowing their analysis in the home environment as well. The methods proposed include (i) specific image processing algorithms for detecting facial parts with periodic temperature changes; (ii) computational intelligence tools for analysing the associated videosequences; and (iii) digital filters and spectral estimation tools for processing the depth matrices. Machine learning applied to thermal imaging camera calibration allowed the recognition of its digital information with an accuracy close to 100% for the classification of individual temperature values. The proposed detection of breathing features was used for monitoring of physical activities by the home exercise bike. The results include a decrease of breathing temperature and its frequency after a load, with mean values −0.16 °C/min and −0.72 bpm respectively, for the given set of experiments. The proposed methods verify that thermal and depth cameras can be used as additional tools for multimodal detection of breathing patterns. View Full-Text
Keywords: thermography; machine learning; facial temperature distribution; depth sensors; multimodal signals; breathing disorders detection thermography; machine learning; facial temperature distribution; depth sensors; multimodal signals; breathing disorders detection
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Procházka, A.; Charvátová, H.; Vyšata, O.; Kopal, J.; Chambers, J. Breathing Analysis Using Thermal and Depth Imaging Camera Video Records. Sensors 2017, 17, 1408.

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