{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T15:59:32Z","timestamp":1775577572809,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,16]],"date-time":"2022-11-16T00:00:00Z","timestamp":1668556800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Medical Faculty of RWTH Aachen University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The ability to continuously and unobtrusively monitor and classify breathing patterns can be very valuable for automated health assessments because respiration is tightly coupled to many physiological processes. Pathophysiological changes in these processes often manifest in altered breathing patterns and can thus be immediately detected. In order to develop a breathing pattern monitoring system, a study was conducted in which volunteer subjects were asked to breathe according to a predefined breathing protocol containing multiple breathing patterns while being recorded with color and thermal cameras. The recordings were used to develop and compare several respiratory signal extraction algorithms. An algorithm for the robust extraction of multiple respiratory features was developed and evaluated, capable of differentiating a wide range of respiratory patterns. These features were used to train a one vs. one multiclass support vector machine, which can distinguish between breathing patterns with an accuracy of 95.79 %. The recorded dataset was published to enable further improvement of contactless breathing pattern classification, especially for complex breathing patterns.<\/jats:p>","DOI":"10.3390\/s22228854","type":"journal-article","created":{"date-parts":[[2022,11,17]],"date-time":"2022-11-17T06:24:42Z","timestamp":1668666282000},"page":"8854","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Breathing Pattern Monitoring by Using Remote Sensors"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9399-706X","authenticated-orcid":false,"given":"Janosch","family":"Kunczik","sequence":"first","affiliation":[{"name":"Department of Anesthesiology, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany"}]},{"given":"Kerstin","family":"Hubbermann","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0041-2879","authenticated-orcid":false,"given":"Lucas","family":"M\u00f6sch","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6817-2472","authenticated-orcid":false,"given":"Andreas","family":"Follmann","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7118-7728","authenticated-orcid":false,"given":"Michael","family":"Czaplik","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1788-4562","authenticated-orcid":false,"given":"Carina","family":"Barbosa Pereira","sequence":"additional","affiliation":[{"name":"Department of Anesthesiology, Faculty of Medicine, RWTH Aachen University, 52074 Aachen, Germany"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e826","DOI":"10.1161\/CIR.0000000000000734","article-title":"2019 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Pediatric Life Support; Neonatal Life Support; Education, Implementation, and Teams; and First Aid Task Forces","volume":"140","author":"Soar","year":"2019","journal-title":"Circulation"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"The ATLS Subcommittee, American College of Surgeons\u2019 Committee on Trauma, and the International ATLS Working Group (2013). 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