Ве молиме користете го овој идентификатор да го цитирате или поврзете овој запис: http://hdl.handle.net/20.500.12188/22027
DC FieldValueLanguage
dc.contributor.authorStankoski, Simonen_US
dc.contributor.authorKiprijanovska, Ivanaen_US
dc.contributor.authorMavridou, Ifigeneiaen_US
dc.contributor.authorNduka, Charlesen_US
dc.contributor.authorGjoreski, Hristijanen_US
dc.contributor.authorGjoreski, Martinen_US
dc.date.accessioned2022-08-09T09:18:14Z-
dc.date.available2022-08-09T09:18:14Z-
dc.date.issued2022-03-08-
dc.identifier.urihttp://hdl.handle.net/20.500.12188/22027-
dc.description.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’s 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>en_US
dc.language.isoenen_US
dc.publisherMDPI AGen_US
dc.relation.ispartofSensorsen_US
dc.titleBreathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learningen_US
dc.identifier.doi10.3390/s22062079-
dc.identifier.urlhttps://www.mdpi.com/1424-8220/22/6/2079/pdf-
dc.identifier.volume22-
dc.identifier.issue6-
item.fulltextNo Fulltext-
item.grantfulltextnone-
Appears in Collections:Faculty of Electrical Engineering and Information Technologies: Journal Articles
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