TY - JOUR
T1 - Calculation of Heartbeat Rate and SpO2 Parameters Using a Smartphone Camera
T2 - Analysis and Testing
AU - Antoniou, Panayiotis
AU - Nestoros, Marios
AU - Polycarpou, Anastasis C.
N1 - Funding Information:
The authors would like to thank the University of Nicosia for the financial support of the project under contract number: Seed Grant/2020/22. https://www.unic.ac.cy.
Publisher Copyright:
© 2023 by the authors.
PY - 2023/1
Y1 - 2023/1
N2 - Mathematical and signal-processing methods were used to obtain reliable measurements of the heartbeat pulse rate and information on oxygen concentration in the blood using short video recordings of the index finger attached to a smartphone built-in camera. Various types of smartphones were used with different operating systems (e.g., iOS, Android) and capabilities. A range of processing algorithms were applied to the red-green-blue (RGB) component signals, including mean intensity calculation, moving average smoothing, and quadratic filtering based on the Savitzky–Golay filter. Two approaches—gradient and local maximum methods—were used to determine the pulse rate, which provided similar results. A fast Fourier transform was applied to the signal to correlate the signal’s frequency components with the pulse rate. We resolved the signal into its DC and AC components to calculate the ratio-of-ratios of the AC and DC components of the red and green signals, a method which is often used to estimate the oxygen concentration in blood. A series of measurements were performed on healthy human subjects, producing reliable data that compared favorably to benchmark data obtained by commercial and medically approved oximeters. Furthermore, the effect of the video recording duration on the accuracy of the results was investigated.
AB - Mathematical and signal-processing methods were used to obtain reliable measurements of the heartbeat pulse rate and information on oxygen concentration in the blood using short video recordings of the index finger attached to a smartphone built-in camera. Various types of smartphones were used with different operating systems (e.g., iOS, Android) and capabilities. A range of processing algorithms were applied to the red-green-blue (RGB) component signals, including mean intensity calculation, moving average smoothing, and quadratic filtering based on the Savitzky–Golay filter. Two approaches—gradient and local maximum methods—were used to determine the pulse rate, which provided similar results. A fast Fourier transform was applied to the signal to correlate the signal’s frequency components with the pulse rate. We resolved the signal into its DC and AC components to calculate the ratio-of-ratios of the AC and DC components of the red and green signals, a method which is often used to estimate the oxygen concentration in blood. A series of measurements were performed on healthy human subjects, producing reliable data that compared favorably to benchmark data obtained by commercial and medically approved oximeters. Furthermore, the effect of the video recording duration on the accuracy of the results was investigated.
KW - blood oxygen concentration
KW - heartbeat rate
KW - smartphone sensing
UR - http://www.scopus.com/inward/record.url?scp=85146400828&partnerID=8YFLogxK
U2 - 10.3390/s23020737
DO - 10.3390/s23020737
M3 - Article
C2 - 36679533
AN - SCOPUS:85146400828
SN - 1424-8220
VL - 23
JO - Sensors
JF - Sensors
IS - 2
M1 - 737
ER -