TY - GEN
T1 - Direct-global separation for improved imaging photoplethysmography
AU - Park, Jaehee
AU - Sabharwal, Ashutosh
AU - Veeraraghavan, Ashok
N1 - Funding Information:
We thank Akash Kumar Maity for helping with the writing of the paper. We also thank all of the subjects that participated in the data collection. This work was partially supported by NSF CAREER Award 1652633, NSF Expeditions Award 1730574 and NIH grant 5R01DK113269-02.
Publisher Copyright:
© 2018 IEEE.
PY - 2018/12/13
Y1 - 2018/12/13
N2 - Camera-based estimation of vital signs has made significant progress in last few years. Despite of the significant algorithmic advances, the low signal-to-background ratio in video-based photoplethysmography continues to be a performance bottleneck. One of the main challenges is that much of the light returning to the camera from the subject is surface reflection from the skin and other dermal layers, and hence does not contain any pulsatile blood perfusion information to estimate photoplesthysmogram (PPG). In this paper, we show that direct-global separation techniques designed to reject much of the surface reflection photons can improve the signal-to-background ratio in the raw captured video signal. We study two techniques for the suppression of direct surface reflection (a) cross-polarization and (b) structured illumination. Using a dataset from 28 participants, our results show an average SNR improvement in estimating PPG from the use of structured illumination is 1.42 dB compared to the brightfield illumination. The use of cross-polarizers leads to an average SNR increase of 1.49 dB compared to brightfield illumination. And the combined structured illumination and polarizer method increases the SNR on the average by 1.90 dB compared to the brightfield illumination. The key result is that local PPG estimate SNR can increase to more than 5.63dB, enabling very large gains on regions with a large specular component. The RMSE decreased 55% and the range of error reduced by 12.9% with the use of a polarizer and structured illumination.
AB - Camera-based estimation of vital signs has made significant progress in last few years. Despite of the significant algorithmic advances, the low signal-to-background ratio in video-based photoplethysmography continues to be a performance bottleneck. One of the main challenges is that much of the light returning to the camera from the subject is surface reflection from the skin and other dermal layers, and hence does not contain any pulsatile blood perfusion information to estimate photoplesthysmogram (PPG). In this paper, we show that direct-global separation techniques designed to reject much of the surface reflection photons can improve the signal-to-background ratio in the raw captured video signal. We study two techniques for the suppression of direct surface reflection (a) cross-polarization and (b) structured illumination. Using a dataset from 28 participants, our results show an average SNR improvement in estimating PPG from the use of structured illumination is 1.42 dB compared to the brightfield illumination. The use of cross-polarizers leads to an average SNR increase of 1.49 dB compared to brightfield illumination. And the combined structured illumination and polarizer method increases the SNR on the average by 1.90 dB compared to the brightfield illumination. The key result is that local PPG estimate SNR can increase to more than 5.63dB, enabling very large gains on regions with a large specular component. The RMSE decreased 55% and the range of error reduced by 12.9% with the use of a polarizer and structured illumination.
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U2 - 10.1109/CVPRW.2018.00186
DO - 10.1109/CVPRW.2018.00186
M3 - Conference contribution
AN - SCOPUS:85060894748
T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
SP - 1456
EP - 1465
BT - Proceedings - 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 31st Meeting of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2018
Y2 - 18 June 2018 through 22 June 2018
ER -