Skip to main content

Heart Rate Measurement Using Remote Photoplethysmograph Based on Skin Segmentation

  • Conference paper
  • First Online:
Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019 (AISI 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1058))

Abstract

The remote Photoplethysmograph (rPPG) is recently used in evaluating heart rate (HR) from registered skin color differences through a camera. It is based on extracting HR from the small periodic color difference in the skin due to heartbeats. Selection of Region of Interest (ROI) is a critical process and its necessity includes as many skin pixels as likely. In this paper, we focus on improving the quality of pulsatile signals by using skin segmentation to select the ROI. So, an automatically enhanced marker built in the watershed algorithm is offered for segmentation of human skin regions from the detected face. The suggested rPPG measurements are evaluated by using root mean square error (RMSE), and complexity time. Based on the simulation result, the proposed algorithm showed that using skin segmentation can significantly improve the performance of the rPPG process.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Da He, D., Winokur, E.S., Sodini, C.G.: A continuous, wearable, and wireless heart monitor using head ballistocardiogram (BCG) and head electrocardiogram (ECG). In: 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 4729–4732 (2011)

    Google Scholar 

  2. Teichmann, D., Brüser, C., Eilebrecht, B., Abbas, A., Blanik, N., Leonhardt, S.: Non-contact monitoring techniques-principles and applications. In: 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1302–1305 (2012)

    Google Scholar 

  3. Al-Naji, A., Gibson, K., Lee, S.-H., Chahl, J.: Monitoring of cardiorespiratory signal: principles of remote measurements and review of methods. IEEE Access 5, 15776–15790 (2017)

    Article  Google Scholar 

  4. Garbey, M., Sun, N., Merla, A., Pavlidis, I.: Contact-free measurement of cardiac pulse based on the analysis of thermal imagery. IEEE Trans. Biomed. Eng. 54, 1418–1426 (2007)

    Article  Google Scholar 

  5. Ulyanov, S.S., Tuchin, V.V.: Pulse-wave monitoring by means of focused laser beams scattered by skin surface and membranes. In: Static and Dynamic Light Scattering in Medicine and Biology, pp. 160–168 (1993)

    Google Scholar 

  6. Kranjec, J., Beguš, S., Drnovšek, J., Geršak, G.: Novel methods for noncontact heart rate measurement: a feasibility study. IEEE Trans. Instrum. Meas. 63, 838–847 (2014)

    Article  Google Scholar 

  7. Fallet, S., Schoenenberger, Y., Martin, L., Braun, F., Moser, V., Vesin, J.-M.: Imaging photoplethysmography: a real-time signal quality index. Comput. Cardiol. (CinC) 2017, 1–4 (2017)

    Google Scholar 

  8. Hertzman, A.B.: Observations on the finger volume pulse recorded photoelectrically. Am. J. Physiol. 119, 334–335 (1937)

    Google Scholar 

  9. Verkruysse, W., Svaasand, L.O., Nelson, J.S.: Remote plethysmographic imaging using ambient light. Opt. Express 16, 21434–21445 (2008)

    Article  Google Scholar 

  10. Poh, M.-Z., McDuff, D.J., Picard, R.W.: Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt. Express 18, 10762–10774 (2010)

    Article  Google Scholar 

  11. Bousefsaf, F., Maaoui, C., Pruski, A.: Continuous wavelet filtering on webcam photoplethysmographic signals to remotely assess the instantaneous heart rate. Biomed. Signal Process. Control 8, 568–574 (2013)

    Article  Google Scholar 

  12. Bobbia, S., Macwan, R., Benezeth, Y., Mansouri, A., Dubois, J.: Unsupervised skin tissue segmentation for remote photoplethysmography. Pattern Recogn. Lett. (2017)

    Google Scholar 

  13. Balakrishnan, G., Durand, F., Guttag, J.: Detecting pulse from head motions in video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3430–3437 (2013)

    Google Scholar 

  14. Shan, L., Yu, M.: Video-based heart rate measurement using head motion tracking and ICA. In: 2013 6th International Congress on Image and Signal Processing (CISP), pp. 160–164 (2013)

    Google Scholar 

  15. Irani, R., Nasrollahi, K., Moeslund, T.B.: Improved pulse detection from head motions using DCT. In: 2014 International Conference on Computer Vision Theory and Applications (VISAPP), pp. 118–124 (2014)

    Google Scholar 

  16. Rouast, P.V., Adam, M.T., Chiong, R., Cornforth, D., Lux, E.: Remote heart rate measurement using low-cost RGB face video: a technical literature review. Front. Comput. Sci. 12, 858–872 (2018)

    Article  Google Scholar 

  17. Poh, M.-Z., McDuff, D.J., Picard, R.W.: Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans. Biomed. Eng. 58, 7–11 (2011)

    Article  Google Scholar 

  18. Wei, L., Tian, Y., Wang, Y., Ebrahimi, T., Huang, T.: Automatic webcam-based human heart rate measurements using Laplacian eigenmap. In: Asian Conference on Computer Vision, pp. 281–292 (2012)

    Google Scholar 

  19. Hsu, Y., Lin, Y.-L., Hsu, W.: Learning-based heart rate detection from remote photoplethysmography features. In: 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4433–4437 (2014)

    Google Scholar 

  20. Fouad, R., Omer, O., Ali, A.-M.M., Aly, M.: Refining ROI selection for real-time remote photoplethysmography using adaptive skin detection (2019)

    Google Scholar 

  21. Das, A., Ghoshal, D.: Human skin region segmentation based on chrominance component using modified watershed algorithm. Procedia Comput. Sci. 89, 856–863 (2016)

    Article  Google Scholar 

  22. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: null, p. 511 (2001)

    Google Scholar 

  23. Becker, B.C., Voros, S., Lobes, L.A., Handa, J.T., Hager, G.D., Riviere, C.N.: Retinal vessel cannulation with an image-guided handheld robot. In: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology, pp. 5420–5423 (2010)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding authors

Correspondence to M. Somaya Abdel-Khier , Osama A. Omer or Hamada Esmaile .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Somaya Abdel-Khier, M., Omer, O.A., Esmaile, H. (2020). Heart Rate Measurement Using Remote Photoplethysmograph Based on Skin Segmentation. In: Hassanien, A., Shaalan, K., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2019. AISI 2019. Advances in Intelligent Systems and Computing, vol 1058. Springer, Cham. https://doi.org/10.1007/978-3-030-31129-2_55

Download citation

Publish with us

Policies and ethics