{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,29]],"date-time":"2026-05-29T19:13:11Z","timestamp":1780081991639,"version":"3.54.0"},"reference-count":47,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,8]],"date-time":"2022-03-08T00:00:00Z","timestamp":1646697600000},"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>Breathing rate is considered one of the fundamental vital signs and a highly informative indicator of physiological state. Given that the monitoring of heart activity is less complex than the monitoring of breathing, a variety of algorithms have been developed to estimate breathing activity from heart activity. However, estimating breathing rate from heart activity outside of laboratory conditions is still a challenge. The challenge is even greater when new wearable devices with novel sensor placements are being used. In this paper, we present a novel algorithm for breathing rate estimation from photoplethysmography (PPG) data acquired from a head-worn virtual reality mask equipped with a PPG sensor placed on the forehead of a subject. The algorithm is based on advanced signal processing and machine learning techniques and includes a novel quality assessment and motion artifacts removal procedure. The proposed algorithm is evaluated and compared to existing approaches from the related work using two separate datasets that contains data from a total of 37 subjects overall. Numerous experiments show that the proposed algorithm outperforms the compared algorithms, achieving a mean absolute error of 1.38 breaths per minute and a Pearson\u2019s correlation coefficient of 0.86. These results indicate that reliable estimation of breathing rate is possible based on PPG data acquired from a head-worn device.<\/jats:p>","DOI":"10.3390\/s22062079","type":"journal-article","created":{"date-parts":[[2022,3,9]],"date-time":"2022-03-09T01:50:53Z","timestamp":1646790653000},"page":"2079","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":30,"title":["Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning"],"prefix":"10.3390","volume":"22","author":[{"given":"Simon","family":"Stankoski","sequence":"first","affiliation":[{"name":"Emteq Ltd., Brighton BN1 9SB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivana","family":"Kiprijanovska","sequence":"additional","affiliation":[{"name":"Emteq Ltd., Brighton BN1 9SB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ifigeneia","family":"Mavridou","sequence":"additional","affiliation":[{"name":"Emteq Ltd., Brighton BN1 9SB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Charles","family":"Nduka","sequence":"additional","affiliation":[{"name":"Emteq Ltd., Brighton BN1 9SB, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0770-4268","authenticated-orcid":false,"given":"Hristijan","family":"Gjoreski","sequence":"additional","affiliation":[{"name":"Emteq Ltd., Brighton BN1 9SB, UK"},{"name":"Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, 1000 Skopje, North Macedonia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1220-7418","authenticated-orcid":false,"given":"Martin","family":"Gjoreski","sequence":"additional","affiliation":[{"name":"Faculty of Informatics, Universit\u00e0 della Svizzera Italiana, 6900 Lugano, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,8]]},"reference":[{"key":"ref_1","unstructured":"Picard, R.W. (2019). Affective Computing, MIT Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"18","DOI":"10.1109\/T-AFFC.2010.1","article-title":"Affect detection: An interdisciplinary review of models, methods, and their applications","volume":"1","author":"Calvo","year":"2010","journal-title":"IEEE Trans. Affect. Comput."},{"key":"ref_3","unstructured":"Mavridou, I., Perry, M., Seiss, E., Kostoulas, T., and Balaguer-Ballester, E. 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