Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 6; p. 2079
• Main Authors: Stankoski, Simon, Kiprijanovska, Ivana, Mavridou, Ifigeneia, Nduka, Charles, Gjoreski, Hristijan, Gjoreski, Martin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 08.03.2022; MDPI
• Subjects: Affect (Psychology); Algorithms; breathing rate; Datasets; Electrocardiography; Heart rate; Heart Rate - physiology; Humans; information fusion; Laboratories; Machine Learning; motion artifact removal; Photoplethysmography - methods; Physiology; PPG; Respiration; Respiratory Rate; Sensors; Signal processing; Signal Processing, Computer-Assisted; Virtual reality; VR headset; Wearable computers; breathing rate; VR headset; information fusion; machine learning; PPG; motion artifact removal
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s22062079

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.