Ве молиме користете го овој идентификатор да го цитирате или поврзете овој запис:
http://hdl.handle.net/20.500.12188/22027
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Stankoski, Simon | en_US |
dc.contributor.author | Kiprijanovska, Ivana | en_US |
dc.contributor.author | Mavridou, Ifigeneia | en_US |
dc.contributor.author | Nduka, Charles | en_US |
dc.contributor.author | Gjoreski, Hristijan | en_US |
dc.contributor.author | Gjoreski, Martin | en_US |
dc.date.accessioned | 2022-08-09T09:18:14Z | - |
dc.date.available | 2022-08-09T09:18:14Z | - |
dc.date.issued | 2022-03-08 | - |
dc.identifier.uri | http://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.iso | en | en_US |
dc.publisher | MDPI AG | en_US |
dc.relation.ispartof | Sensors | en_US |
dc.title | Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning | en_US |
dc.identifier.doi | 10.3390/s22062079 | - |
dc.identifier.url | https://www.mdpi.com/1424-8220/22/6/2079/pdf | - |
dc.identifier.volume | 22 | - |
dc.identifier.issue | 6 | - |
item.fulltext | No Fulltext | - |
item.grantfulltext | none | - |
Appears in Collections: | Faculty of Electrical Engineering and Information Technologies: Journal Articles |
Записите во DSpace се заштитени со авторски права, со сите права задржани, освен ако не е поинаку наведено.