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Effects of illuminance intensity on the green channel of remote photoplethysmography (rPPG) signals

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Abstract

Point-of-care remote photoplethysmography (rPPG) devices that utilize low-cost RGB cameras have drawn considerable attention due to their convenience in contactless and non-invasive vital signs monitoring. In rPPG, sufficient lighting conditions are essential for obtaining accurate diagnostics by observing the complete signal morphology. The effects of illuminance intensity and light source settings play a significant role in rPPG assessment quality, and it was previously observed that different lighting schemes result in different signal quality and morphology. This study presents a quantitative empirical analysis where the quality and morphology of rPPG signals were assessed under different light settings. Participants’ faces were exposed to the white LED spotlight, first when the sources were installed directly behind the video camera, and then when the sources were installed in a cross-polarized scheme. Hence, the effect of specular reflectance on rPPG signals could be observed in an increasing projection. The signal qualities were analyzed in each intensity level using a signal-to-noise (SNR) ratio metric. In 3 of 7 participants, placing the video camera on the same level as the light source led to signal quality loss of up to 3 dB for the range 30–60 Lux. In addition, two fundamental morphological features were analyzed, and the derivative-related feature was found to be increasing with illuminance intensity in 6 of 7 participants.

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References

  1. Yang, Y, Wang H, Jiang R, Guo X, Cheng J, Chen Y (2022) A review of IoT-enabled mobile healthcare: technologies, challenges, and future trends. IEEE Internet Things J 9(12):9478–9502. https://doi.org/10.1109/JIOT.2022.3144400.

    Article  Google Scholar 

  2. Pradhan B, Bhattacharyya S, Pal K (2021) IoT-based applications in healthcare devices. J Healthcare Eng 2021:6632599. https://doi.org/10.1155/2021/6632599

    Article  Google Scholar 

  3. Acar G, Ozturk O, Golparvar AJ et al (2019) Wearable and flexible textile electrodes for biopotential signal monitoring: a review. Electronics 8(5):479. https://doi.org/10.3390/electronics8050479

    Article  CAS  Google Scholar 

  4. Rouast PV, Adam MTP, Chiong R et al (2018) Remote heart rate measurement using low-cost RGB face video: a technical literature review. Front Comput Sci 12(5):858–872. https://doi.org/10.1007/s11704-016-6243-6

    Article  Google Scholar 

  5. Kamshilin AA, Margaryants NB (2017) Origin of photoplethysmographic waveform at green light. Phys Procedia 86:72–80. https://doi.org/10.1016/j.phpro.2017.01.024

    Article  Google Scholar 

  6. Allen J (2007) Photoplethysmography and its application in clinical physiological measurement. Physiol Meas 28(3):R1–R39. https://doi.org/10.1088/0967-3334/28/3/R01

    Article  Google Scholar 

  7. Sun Y, Thakor N (2016) Photoplethysmography revisited: from contact to noncontact, from point to imaging. IEEE Trans Biomed Eng 63(3):463–477. https://doi.org/10.1109/TBME.2015.2476337

    Article  Google Scholar 

  8. Verkruysse W, Svaasand LO, Nelson JS (2008) Remote plethysmographic imaging using ambient light. Opt Express 16(26):21434–21445. https://doi.org/10.1364/oe.16.021434

    Article  CAS  Google Scholar 

  9. Sinhal R, Singh K, Raghuwanshi MM (2020) An overview of remote photoplethysmography methods for vital sign monitoring. In: Gupta M, Konar D, Bhattacharyya S et al (eds) Computer vision and machine intelligence in medical image analysis, vol 992. Springer, Singapore, pp 21–31

    Chapter  Google Scholar 

  10. Eaton A, Vishwanath K, Cheng CH et al (2018) Lock-in technique for extraction of pulse rates and associated confidence levels from video. Appl Opt 57(16):4360. https://doi.org/10.1364/AO.57.004360

    Article  Google Scholar 

  11. Xu S, Sun L, Rohde GK (2014) Robust efficient estimation of heart rate pulse from video. Biomed Opt Express 5(4):1124. https://doi.org/10.1364/BOE.5.001124

    Article  Google Scholar 

  12. Wang W, Stuijk S, de Haan G (2016) A novel algorithm for remote photoplethysmography: spatial subspace rotation. IEEE Trans Biomed Eng 63(9):1974–1984. https://doi.org/10.1109/TBME.2015.2508602

    Article  Google Scholar 

  13. de Haan G, van Leest A (2014) Improved motion robustness of remote-PPG by using the blood volume pulse signature. Physiol Meas 35(9):1913–1926. https://doi.org/10.1088/0967-3334/35/9/1913

    Article  Google Scholar 

  14. de Haan G, Jeanne V (2013) Robust pulse rate from chrominance-based rPPG. IEEE Trans Biomed Eng 60(10):2878–2886. https://doi.org/10.1109/TBME.2013.2266196

    Article  Google Scholar 

  15. Poh MZ, McDuff DJ, Picard RW (2010) Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt Express 18(10):10762. https://doi.org/10.1364/OE.18.010762

    Article  CAS  Google Scholar 

  16. Macwan R, Benezeth Y, Mansouri A (2019) Heart rate estimation using remote photoplethysmography with multi-objective optimization. Biomed Signal Process Control 49:24–33. https://doi.org/10.1016/j.bspc.2018.10.012

    Article  Google Scholar 

  17. Tsouri GR, Kyal S, Dianat S et al (2012) Constrained independent component analysis approach to nonobtrusive pulse rate measurements. J Biomed Opt 17(7):0770111. https://doi.org/10.1117/1.JBO.17.7.077011

    Article  Google Scholar 

  18. Wang W, Shan C (2019) Impact of makeup on remote-PPG monitoring. Biomed Phys Eng Express. https://doi.org/10.1088/2057-1976/ab51ba

    Article  Google Scholar 

  19. Wang W, den Brinker AC, Stuijk S et al (2016) Algorithmic principles of remote PPG. IEEE Trans Biomed Eng 64(7):1479–1491. https://doi.org/10.1109/TBME.2016.2609282

    Article  Google Scholar 

  20. Tominaga S (1994) Dichromatic reflection models for a variety of materials. Color Res Appl 19(4):277–285. https://doi.org/10.1002/col.5080190408

    Article  Google Scholar 

  21. Jeanne V, Asselman M, den Brinker B, et al (2013) Camera-based heart rate monitoring in highly dynamic light conditions. In: 2013 International Conference on Connected Vehicles and Expo (ICCVE). IEEE, Las Vegas, NV, USA, pp 798–799. https://doi.org/10.1109/ICCVE.2013.6799899. http://ieeexplore.ieee.org/document/6799899/

  22. Li X, Chen J, Zhao G, et al (2014) Remote heart rate measurement from face videos under realistic situations. In: 2014 IEEE conference on computer vision and pattern recognition. IEEE, Columbus, OH, USA, pp 4264–4271. https://doi.org/10.1109/CVPR.2014.543. http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=6909939

  23. Tulyakov S, Alameda-Pineda X, Ricci E, et al (2016) Self-adaptive matrix completion for heart rate estimation from face videos under realistic conditions. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR). IEEE, Las Vegas, NV, USA, pp 2396–2404. https://doi.org/10.1109/CVPR.2016.263. http://ieeexplore.ieee.org/document/7780632/

  24. Wu BF, Chu YW, Huang PW et al (2019) Neural network based luminance variation resistant remote-photoplethysmography for driver’s heart rate monitoring. IEEE Access 7:57210–57225. https://doi.org/10.1109/ACCESS.2019.2913664

    Article  Google Scholar 

  25. Blackford EB, Estepp JR, McDuff DJ (2018) Remote spectral measurements of the blood volume pulse with applications for imaging photoplethysmography. In: Coté GL (ed) Optical diagnostics and sensing XVIII: toward point-of-care diagnostics. SPIE, San Francisco, p 41

    Google Scholar 

  26. Luo H, Yang D, Barszczyk A et al (2019) Smartphone-based blood pressure measurement using transdermal optical imaging technology. Circulation 12(8):e008857. https://doi.org/10.1161/CIRCIMAGING.119.008857

    Article  Google Scholar 

  27. Nowara EM, McDuff D, Veeraraghavan A (2020) A meta-analysis of the impact of skin tone and gender on non-contact photoplethysmography measurements. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) workshops

  28. Lempe G, Zaunseder S, Wirthgen T et al (2013) ROI selection for remote photoplethysmography. In: Meinzer HP, Deserno TM, Handels H et al (eds) Bildverarbeitung für die Medizin 2013. Springer, Berlin, pp 99–103

    Chapter  Google Scholar 

  29. Sungjun Kwon, Jeehoon Kim, Dongseok Lee, et al (2015) ROI analysis for remote photoplethysmography on facial video. In: 2015 37th Annual international conference of the IEEE engineering in medicine and biology society (EMBC). IEEE, Milan, pp 4938–4941. https://doi.org/10.1109/EMBC.2015.7319499. http://ieeexplore.ieee.org/document/7319499/

  30. Rong M, Li K (2021) A multi-type features fusion neural network for blood pressure prediction based on photoplethysmography. Biomed Signal Process Control 68(102):772. https://doi.org/10.1016/j.bspc.2021.102772

    Article  Google Scholar 

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Funding

This study was partially funded by the Sabanci University research fund.

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Authors and Affiliations

  1. Department of Electronics Engineering, Faculty of Engineering and Natural Sciences, Sabanci University, Tuzla, 34956, Istanbul, Turkey

    Saygun Guler, Ozberk Ozturk, Ata Golparvar & Murat Kaya Yapici

  2. Integrated Circuits Laboratory (ICLAB), École Polytechnique Fédérale de Lausanne (EPFL), 2002, Neuchêtel, Switzerland

    Ata Golparvar

  3. Department of Computing and Informatics, Bournemouth University, Fern Barrow, Bournemouth, Dorset, BH12 5BB, UK

    Huseyin Dogan

  4. Department of Electrical Engineering, University of Washington, Washington, 98195, USA

    Murat Kaya Yapici

Authors
  1. Saygun Guler
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  2. Ozberk Ozturk
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  3. Ata Golparvar
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  4. Huseyin Dogan
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  5. Murat Kaya Yapici
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Corresponding author

Correspondence to Saygun Guler.

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The authors declare no conflicts of interest.

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The experiments were conducted in Bournemouth University (BU) and The Research Ethics Panel of BU approved the study which was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments.

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The informed consent of each participant was obtained prior to the sessions. The authors affirm that all participants provided informed consent for publication of the images and relevant data that were collected during the experiments.

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Guler, S., Ozturk, O., Golparvar, A. et al. Effects of illuminance intensity on the green channel of remote photoplethysmography (rPPG) signals. Phys Eng Sci Med 45, 1317–1323 (2022). https://doi.org/10.1007/s13246-022-01175-7

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  • DOI: https://doi.org/10.1007/s13246-022-01175-7

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