TY - JOUR
T1 - HRVCam
T2 - Robust camera-based measurement of heart rate variability
AU - Pai, Amruta
AU - Veeraraghavan, Ashok
AU - Sabharwal, Ashutosh
N1 - Funding Information:
This work was partially supported by NSF Engineering Research Center for Precise Advanced Technologies and Health Systems for Underserved Populations (PATHS-UP) (Award No. 1648451). We would also like to thank Dr. Mayank Kumar for his valuable suggestions and discussions. We acknowledge the previous SPIE Proceedings publication titled “CameraHRV: robust measurement of HRV using a camera,”30 which presented a preliminary version of the method presented in this paper.
Publisher Copyright:
© The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.
PY - 2021/2/1
Y1 - 2021/2/1
N2 - Significance: Non-contact, camera-based heart rate variability estimation is desirable in numerous applications, including medical, automotive, and entertainment. Unfortunately, camera-based HRV accuracy and reliability suffer due to two challenges: (a) darker skin tones result in lower SNR and (b) relative motion induces measurement artifacts. Aim: We propose an algorithm HRVCam that provides sufficient robustness to low SNR and motion-induced artifacts commonly present in imaging photoplethysmography (iPPG) signals. Approach: HRVCam computes camera-based HRV from the instantaneous frequency of the iPPG signal. HRVCam uses automatic adaptive bandwidth filtering along with discrete energy separation to estimate the instantaneous frequency. The parameters of HRVCam use the observed characteristics of HRV and iPPG signals. Results: We capture a new dataset containing 16 participants with diverse skin tones. We demonstrate that HRVCam reduces the error in camera-based HRV metrics significantly (more than 50% reduction) for videos with dark skin and face motion. Conclusion: HRVCam can be used on top of iPPG estimation algorithms to provide robust HRV measurements making camera-based HRV practical.
AB - Significance: Non-contact, camera-based heart rate variability estimation is desirable in numerous applications, including medical, automotive, and entertainment. Unfortunately, camera-based HRV accuracy and reliability suffer due to two challenges: (a) darker skin tones result in lower SNR and (b) relative motion induces measurement artifacts. Aim: We propose an algorithm HRVCam that provides sufficient robustness to low SNR and motion-induced artifacts commonly present in imaging photoplethysmography (iPPG) signals. Approach: HRVCam computes camera-based HRV from the instantaneous frequency of the iPPG signal. HRVCam uses automatic adaptive bandwidth filtering along with discrete energy separation to estimate the instantaneous frequency. The parameters of HRVCam use the observed characteristics of HRV and iPPG signals. Results: We capture a new dataset containing 16 participants with diverse skin tones. We demonstrate that HRVCam reduces the error in camera-based HRV metrics significantly (more than 50% reduction) for videos with dark skin and face motion. Conclusion: HRVCam can be used on top of iPPG estimation algorithms to provide robust HRV measurements making camera-based HRV practical.
KW - Heart rate variability
KW - Imaging photoplethysmography
KW - Noncontact HRV
KW - Pulse frequency demodulation
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U2 - 10.1117/1.JBO.26.2.022707
DO - 10.1117/1.JBO.26.2.022707
M3 - Article
C2 - 33569935
AN - SCOPUS:85101480646
SN - 1083-3668
VL - 26
JO - Journal of Biomedical Optics
JF - Journal of Biomedical Optics
IS - 2
M1 - 022707
ER -