{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,1]],"date-time":"2026-03-01T00:47:35Z","timestamp":1772326055935,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2017,6,16]],"date-time":"2017-06-16T00:00:00Z","timestamp":1497571200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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 \u22120.16 \u00b0C\/min and \u22120.72 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.<\/jats:p>","DOI":"10.3390\/s17061408","type":"journal-article","created":{"date-parts":[[2017,6,16]],"date-time":"2017-06-16T10:06:31Z","timestamp":1497607591000},"page":"1408","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":63,"title":["Breathing Analysis Using Thermal and Depth Imaging Camera Video Records"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0270-1738","authenticated-orcid":false,"given":"Ale\u0161","family":"Proch\u00e1zka","sequence":"first","affiliation":[{"name":"Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague, Czech Republic"}]},{"given":"Hana","family":"Charv\u00e1tov\u00e1","sequence":"additional","affiliation":[{"name":"Faculty of Applied Informatics, Tomas Bata University in Zl\u00edn, 760 05 Zl\u00edn, Czech Republic"}]},{"given":"Old\u0159ich","family":"Vy\u0161ata","sequence":"additional","affiliation":[{"name":"Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague, Czech Republic"},{"name":"Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, 166 36 Prague, Czech Republic"},{"name":"Faculty of Medicine in Hradec Kr\u00e1lov\u00e9, Department of Neurology, Charles University, 500 05 Hradec Kralove, Czech Republic"}]},{"given":"Jakub","family":"Kopal","sequence":"additional","affiliation":[{"name":"Department of Computing and Control Engineering, University of Chemistry and Technology in Prague, 166 28 Prague, Czech Republic"}]},{"given":"Jonathon","family":"Chambers","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Newcastle University, Newcastle upon Tyne, NE1 7RU, UK"}]}],"member":"1968","published-online":{"date-parts":[[2017,6,16]]},"reference":[{"key":"ref_1","first-page":"1","article-title":"Microsoft kinect visual and depth sensors for breathing and heart rate analysis","volume":"16","author":"Schatz","year":"2016","journal-title":"Sensors"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Lee, J., Hong, M., and Ryu, S. (2015). Sleep monitoring system using kinect sensor. Int. J. Distrib. Sens. Netw., 2015.","DOI":"10.1155\/2015\/875371"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"169","DOI":"10.1016\/j.dsp.2015.05.011","article-title":"Bayesian classification and analysis of gait disorders using image and depth sensors of Microsoft Kinect","volume":"47","author":"Schatz","year":"2015","journal-title":"Digit. Signal Prog."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1007\/s00521-015-1827-x","article-title":"Use of Image and depth sensors of the Microsoft Kinect for the detection of gait disorders","volume":"26","author":"Schatz","year":"2015","journal-title":"Neural Comput. Appl."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/MSP.2015.2489978","article-title":"Sensors in assisted living","volume":"33","author":"Erden","year":"2016","journal-title":"IEEE Signal Process. Mag."},{"key":"ref_6","first-page":"1278","article-title":"Extraction of breathing features using MS Kinect for sleep stage detection","volume":"10","author":"Centonze","year":"2016","journal-title":"Signal Image Video Process."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Appel, V., Belini, V., Jong, D., Magalh\u00e3es, D., and Caurin, G. (2014, January 12\u201314). Classifying emotions in rehabilitation robotics based on facial skin temperature. Proceedings of the IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics, Sao Paulo, Brazil.","DOI":"10.1109\/BIOROB.2014.6913789"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Boccanfuso, L., Wang, Q., Leite, I., Li, B., Torres, C., Chen, L., Salomons, N., Foster, C., Barney, E., and Ahn, Y. (2016, January 26\u201331). A thermal emotion classifier for improved human\u2013robot interaction. Proceedings of the 25th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN), New York, NY, USA.","DOI":"10.1109\/ROMAN.2016.7745198"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Kwa\u015bniewska, A., and Rumi\u0144ski, J. (2016, January 4\u20138). Face detection in image sequences using a portable thermal camera. Proceedings of the 13th Quantitative Infrared Thermography Conference, Gdansk, Poland.","DOI":"10.21611\/qirt.2016.071"},{"key":"ref_10","first-page":"345","article-title":"Emotion detection from thermal facial imprint based on GLCM features","volume":"11","author":"Latif","year":"2016","journal-title":"ARPN-JEAS"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Nguyen, H., Kotani, K., Chen, F., and Le, B. (2013, January 26\u201327). Estimation of human emotions using thermal facial information. Proceedings of the SPIE\u2014The International Society for Optical Engineering, ICGIP 2013, Hong Kong, China.","DOI":"10.1117\/12.2050206"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1109\/T-AFFC.2012.33","article-title":"Facial expression recognition in the encrypted domain based on local fisher discriminant analysis","volume":"4","author":"Rahulamathavan","year":"2013","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Cheong, Y., Yap, V., and Nisar, H. (2014, January 7\u20138). A novel face detection algorithm using thermal imaging. Proceedings of the 2014 IEEE Symposium on Computer Applications and Industrial Electronics, ISCAIE, Penang, Malaysia.","DOI":"10.1109\/ISCAIE.2014.7010239"},{"key":"ref_14","unstructured":"Liu, P., and Yin, L. (2015, January 4\u20138). Spontaneous facial expression analysis based on temperature changes and head motions. Proceedings of the11th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition, FG 2015, Ljubljana, Slovenia."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2015\/984353","article-title":"Thermal infrared imaging-based computational psychophysiology for psychometrics","volume":"2015","author":"Cardone","year":"2015","journal-title":"Comput. Math. Method Med."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"951","DOI":"10.1111\/psyp.12243","article-title":"Thermal infrared imaging in psychophysiology: Potentialities and limits","volume":"51","author":"Ioannou","year":"2014","journal-title":"Psychophysiology"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"979","DOI":"10.1109\/TBME.2009.2035926","article-title":"Classifying affective states using thermal infrared imaging of the human face","volume":"57","author":"Nhan","year":"2010","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1369","DOI":"10.1007\/s00371-015-1164-1","article-title":"Real-time stress assessment using thermal imaging","volume":"32","author":"Hong","year":"2016","journal-title":"Vis. Comput."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Engert, V., Merla, A., Grant, J., Cardone, D., Tusche, A., and Singer, T. (2014). Exploring the use of thermal infrared imaging in human stress research. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0090782"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Kim, H., Kim, J.-Y., and Im, C.-H. (2016). Fast and robust real-time estimation of respiratory rate from photoplethysmography. Sensors, 16.","DOI":"10.3390\/s16091494"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.bspc.2017.02.003","article-title":"Respiratory rate estimation from the photoplethysmogram via joint sparse signal reconstruction and spectra Psion","volume":"35","author":"Zhang","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1117\/1.JBO.22.3.036006","article-title":"Synergetic use of thermal and visible imaging techniques for contactless and unobtrusive breathing measurement","volume":"22","author":"Hu","year":"2017","journal-title":"J. Biomed. Opt."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.bspc.2017.03.009","article-title":"Wavelet-based embedded algorithm for respiratory rate estimation from PPG signal","volume":"36","author":"Lin","year":"2017","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1016\/j.chest.2016.11.013","article-title":"Validation of the exhaled breath temperature measure: Reference values in healthy subjects","volume":"151","author":"Carpagnano","year":"2017","journal-title":"Chest"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"523","DOI":"10.1002\/ppul.21416","article-title":"Respiration rate monitoring methods: A review","volume":"46","author":"Khalidi","year":"2011","journal-title":"Pediatr. Pulmonol."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Adib, F., Mao, H., Kabelac, Z., Katabi, D., and Miller, R.C. (2015, January 18\u201323). Smart homes that monitor breathing and heart rate. Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, CHI 2015, Seoul, Korea.","DOI":"10.1145\/2702123.2702200"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Heck, D.H., McAfee, S.S., Liu, Y., Babajani-Feremi, A., Rezaie, R., Freeman, W.J., Wheless, J.W., Papanicolaou, A.C., Ruszinko, M., Sokolov, Y., and Kozma, R. (2017). Breathing as a fundamental rhytm of brain function. Front. Neural Circuits, 10.","DOI":"10.3389\/fncir.2016.00115"},{"key":"ref_28","unstructured":"Murthy, R., Pavlidis, I., and Tsiamyrtzis, P. (2004, January 1\u20135). Touchless Monitoring of breathing function. Proceedings of the 26th Annual International Conference of the IEEE EMBS, San Francisco, CA, USA."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"381","DOI":"10.14257\/ijca.2015.8.6.37","article-title":"Pattern recognition of thermal images for monitoring of breathing function","volume":"8","author":"Alqatawna","year":"2015","journal-title":"Int. J. Control Autom."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/BF02348078","article-title":"Critical review of non-invasive respiratory monitoring in medical care","volume":"41","author":"Folke","year":"2003","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"12305","DOI":"10.3390\/s140712305","article-title":"Infrared Thermography for temperature measurement and non-destructive testing","volume":"14","author":"Usamentiaga","year":"2014","journal-title":"Sensors"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2682","DOI":"10.1118\/1.4704644","article-title":"A real-time respiratory motion monitoring system using microsoft kinect sensor","volume":"39","author":"Xia","year":"2012","journal-title":"Med. Phys."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1007\/s11325-012-0757-4","article-title":"Assessment of a wireless headband for automatic sleep scoring","volume":"17","author":"Griessenberger","year":"2013","journal-title":"Sleep Breath."},{"key":"ref_34","unstructured":"Pauly, M., and Greiner, G. (2009). Time-of-flight sensors in computer graphics. Eurographics 2009\u2014State of the Art Reports, The Eurographics Association."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1007\/s11760-016-0928-z","article-title":"GPS-based analysis of physical activities using positioning and heart rate cycling data","volume":"11","author":"Vaseghi","year":"2017","journal-title":"Signal Image Video Process."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/6\/1408\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T18:39:16Z","timestamp":1760207956000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/17\/6\/1408"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,6,16]]},"references-count":35,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2017,6]]}},"alternative-id":["s17061408"],"URL":"https:\/\/doi.org\/10.3390\/s17061408","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,6,16]]}}}