Breathing rate estimation from head-worn photoplethysmography sensor data using machine learning
Informazioni aggiuntive
Autori
Stankoski S.,
Kiprijanovska I.,
Mavridou I.,
Nduka C.,
Gjoreski H.,
Gjoreski M.
Tipo
Articolo pubblicato in rivista scientifica
Anno
2022
Lingua
Inglese
Sommario
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.
Parole chiave
Breathing rate , Machine learning, PPG , VR headset , Motion artifact removal , Information fusion
Periodico
Sensors
Volume
22
Numero ( Mese )
6
Pagine (o numero dell’articolo)
2079
Diffusione
Licenza
CC BY
Visibilità
Pubblico
Status open access
Gold
