Please use this identifier to cite or link to this item: 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
Show simple item record

Page view(s)

41
checked on May 3, 2025

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.