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Breathing Rate Estimation from Head-Worn Photoplethysmography Sensor Data Using Machine Learning
- Source :
- Sensors; Volume 22; Issue 6; Pages: 2079
- Publication Year :
- 2022
- Publisher :
- Multidisciplinary Digital Publishing Institute, 2022.
-
Abstract
- 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.
- Subjects :
- Machine Learning
Respiratory Rate
Heart Rate
Humans
breathing rate
machine learning
PPG
VR headset
motion artifact removal
information fusion
Signal Processing, Computer-Assisted
Electrical and Electronic Engineering
Photoplethysmography
Biochemistry
Instrumentation
Atomic and Molecular Physics, and Optics
Analytical Chemistry
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Database :
- OpenAIRE
- Journal :
- Sensors; Volume 22; Issue 6; Pages: 2079
- Accession number :
- edsair.doi.dedup.....d9bf69c581386b8499009e22d959ec58
- Full Text :
- https://doi.org/10.3390/s22062079