TY - JOUR
T1 - Remote real-time heart rate monitoring with recursive motion artifact removal using PPG signals from a smartphone camera
AU - Hosni, Asmaa
AU - Atef, Mohamed
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
PY - 2023/5
Y1 - 2023/5
N2 - Remote photoplethysmography (rPPG) recorded by low-cost smartphone cameras is a promising method for noncontact monitoring of heart rate (HR). The main challenges of this method are the limited rPPG signal strength and motion artifacts (MAs). To obtain a clean signal while preserving photoplethysmography features, this study proposes algorithms to eliminate the noise and distortions of the extracted rPPG signal. To obtain the best point for the rPPG signal extraction, the highest green channel difference in two consecutive frames is calculated. Mexican hat wavelet transform decomposition is used for MA distortion elimination. Furthermore, we propose a recursive baseline-wander removal algorithm with an adaptive window to effectively remove baseline drift. The peak detection accuracy is enhanced by using an adaptive sliding-window size based on the detected fast Fourier transform beat frequency. Our proposed algorithm was validated using a range of HR values from seven subjects. The results of the algorithm validation showed a real-time operation and accuracy improvements of more than 37.5% for peak detection in the worst case. The proposed method can measure HR remotely from a 0.4 m distance without any additional sensors, achieving a mean absolute error ± standard deviation of 3.58 ± 2.4.
AB - Remote photoplethysmography (rPPG) recorded by low-cost smartphone cameras is a promising method for noncontact monitoring of heart rate (HR). The main challenges of this method are the limited rPPG signal strength and motion artifacts (MAs). To obtain a clean signal while preserving photoplethysmography features, this study proposes algorithms to eliminate the noise and distortions of the extracted rPPG signal. To obtain the best point for the rPPG signal extraction, the highest green channel difference in two consecutive frames is calculated. Mexican hat wavelet transform decomposition is used for MA distortion elimination. Furthermore, we propose a recursive baseline-wander removal algorithm with an adaptive window to effectively remove baseline drift. The peak detection accuracy is enhanced by using an adaptive sliding-window size based on the detected fast Fourier transform beat frequency. Our proposed algorithm was validated using a range of HR values from seven subjects. The results of the algorithm validation showed a real-time operation and accuracy improvements of more than 37.5% for peak detection in the worst case. The proposed method can measure HR remotely from a 0.4 m distance without any additional sensors, achieving a mean absolute error ± standard deviation of 3.58 ± 2.4.
KW - Baseline wander
KW - Heart rate
KW - Motion artifacts
KW - Remote Photoplethysmography
KW - Wavelet transforms
UR - http://www.scopus.com/inward/record.url?scp=85147117660&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85147117660&partnerID=8YFLogxK
U2 - 10.1007/s11042-023-14399-w
DO - 10.1007/s11042-023-14399-w
M3 - Article
AN - SCOPUS:85147117660
SN - 1380-7501
VL - 82
SP - 20571
EP - 20588
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 13
ER -