Stankoski, Simon Kiprijanovska, Ivana Mavridou, Ifigeneia Nduka, Charles Gjoreski, Hristijan Gjoreski, Martin 2022 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. application/pdf https://n2t.net/ark:/12658/srd1331988 https://susi.usi.ch/global/documents/331988 https://susi.usi.ch/documents/331988/files/Gjoreski_2022_MDPI_Sensors_Breathing Rate Estimation.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.3390/s22062079 info:eu-repo/semantics/altIdentifier/ark/12658/srd1331988 info:eu-repo/semantics/openAccess CC BY Sensors. - 2022, vol. 22, no. 6, p. 2079 Breathing rate Machine learning PPG VR headset Motion artifact removal Information fusion info:eu-repo/classification/udc/004 Breathing rate estimation from head-worn photoplethysmography sensor data using machine learning http://purl.org/coar/resource_type/c_6501