Skip to main content
SpringerLink
Log in

Color Signal Processing Methods for Webcam-Based Heart Rate Evaluation

  • Conference paper
  • First Online:
Intelligent Systems and Applications (IntelliSys 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1038))

Included in the following conference series:

Abstract

Computer vision methods are widely applied in health assistance and medical diagnostics. Photoplethysmography (PPG) is one such method that can be used for contactless estimation of heart rate through the analysis of slight variations of skin color which are caused by changes in the blood volume in vessels. These changes of skin color registered by a camera are called color signal. According to recent studies some PPG methods can be applied on video data recorded by common web-cameras with sufficient accuracy, so they are recognized as potentially applicable for long-term health monitoring in house or office conditions. In this study, we evaluate the accuracy of commonly used signal processing methods for webcam-based PPG as well as novel modifications of these methods in various combinations with preprocessing and postprocessing filtering algorithms. In particular, the Extended Fourier analysis that is based on Gaussian smoothing and temporal averaging of Fourier spectra was applied to estimate heart rate.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
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

Notes

  1. 1.

    https://osf.io/fdrbh.

References

  1. Allen, J.: Photoplethysmography and its application in clinical physiological measurement. Physiol. Measur. 28(3), R1 (2007)

    Article  Google Scholar 

  2. Ans, B., Herault, J., Jutten, C.: Adaptive neural architectures: detection of primitives. In: Proceedings of COGNITIVA 1985, pp 593–597 (1985)

    Google Scholar 

  3. Asthana, A., Zafeiriou, S., Cheng, S., Pantic, M.: Robust discriminative response map fitting with constrained local models. In: CVPR, pp. 3444–3451 (2013). https://doi.org/10.1109/CVPR.2013.442

  4. Balakrishnan, G., Durand, F., Guttag, J.: Detecting pulse from head motions in video. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 3430–3437 (2013). https://doi.org/10.1109/CVPR.2013.440

  5. Bouguet, J.Y.: Pyramidal implementation of the Lucas Kanade Feature Tracker Description of the algorithm. Intel Corporation, Technical report (2001)

    Google Scholar 

  6. Emrah Tasli, H., Gudi, A., den Uyl, M.: Remote PPG based vital sign measurement using adaptive facial regions. IEEE ICIP 2014, 1410–1414 (2015). https://doi.org/10.1109/ICIP.2014.7025282

    Article  Google Scholar 

  7. Haan, G.D., Jeanne, V.: Robust pulse-rate from chrominance-based rPPG. IEEE Trans. Biomed. Eng. 60(10), 1–9 (2013)

    Article  Google Scholar 

  8. Holton, B., Mannapperuma, K., Lesniewski, P., Thomas, J.: Signal recovery in imaging photoplethysmography. Physiol. Measur. 34(11), 1499–1511 (2013). https://doi.org/10.1088/0967-3334/34/11/1499

    Article  Google Scholar 

  9. Irani, R., Nasrollahi, K., Moeslund, T.B.: Improved pulse detection from head motions using DCT. In: IIIE VISAPP, vol. 3, pp. 118–124 (2014)

    Google Scholar 

  10. Kopeliovich, M., Petrushan, M., Shaposhnikov, D.: Approximation-based transformation of color signal for heart rate estimation with a webcam. Pattern Recogn. Image Anal. 28(4), 646–651 (2018)

    Article  Google Scholar 

  11. Kopeliovich, M.V., Petrushan, M.V.: Optimal facial areas for webcam-based photoplethysmography. Pattern Recogn. Image Anal. 26(1), 150–154 (2016). https://doi.org/10.1134/S1054661816010120

    Article  Google Scholar 

  12. Kumar, M., Veeraraghavan, A., Sabharwal, A.: DistancePPG: robust non-contact vital signs monitoring using a camera. Biomed. Opt. Express 6(5), 1565 (2015). https://doi.org/10.1364/BOE.6.001565

    Article  Google Scholar 

  13. Li, X., Chen, J., Zhao, G., Pietik, M.: Remote heart rate measurement from face videos under realistic situations. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2014)

    Google Scholar 

  14. Lienhart, R., Maydt, J.: An extended set of Haar- like features for rapid object detection. In: ICIP, pp. 900–903 (2002)

    Google Scholar 

  15. van Luijtelaar, R., Wang, W., Stuijk, S., de Haan, G.: Automatic ROI detection for camera-based pulse-rate measurement. In: Asian Conference on Computer Vision, pp 360–374 (2014). https://doi.org/10.1007/978-3-319-16631-5_27

    Chapter  Google Scholar 

  16. Macwan, R., Benezeth, Y., Mansouri, A.: Remote photoplethysmography with constrained ICA using periodicity and chrominance constraints. BioMed. Eng. Online 17(1), 22 (2018). https://doi.org/10.1186/s12938-018-0450-3

    Article  Google Scholar 

  17. McDuff, D.: Advancements in remote physiological measurement and applications in human- computer interaction. In: SPIE, vol. 10251, p. 102510V (2017). https://doi.org/10.1117/12.2276026

  18. McDuff, D., Blackford, E.B., Estepp, J.: Fusing partial camera signals for non-contact pulse rate variability measurement. IEEE Trans. Biomed. Eng. 1 (2017). https://doi.org/10.1109/TBME.2017.2771518

  19. Mcintyre, S., Eklund, J.M., Collins, C.: Using visual analytics of heart rate variation to aid in diagnostics. In: AVI, pp. 20–27 (2016)

    Google Scholar 

  20. OpenCV: OpenCV 2.4.6.0. (2013). http://docs.opencv.org/2.4.6

  21. Pearson, K.: On lines and planes of closest fit to systems of points in space. Philos. Mag. 2, 559–572 (1901)

    Article  Google Scholar 

  22. Poh, M.Z., McDuff, D.J., Picard, R.W.: Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Trans. Biomed. Eng. 58(1), 7–11 (2011). https://doi.org/10.1109/TBME.2010.2086456

    Article  Google Scholar 

  23. Robergs, R.A., Landwehr, R.: The surprising history of the “HRmax=220-age” equation. J. Exerc. Physiol. 1971(1), 1–10, (2002). ISSN 1097-9751

    Google Scholar 

  24. Rundo, F., Conoci, S., Ortis, A., Battiato, S.: An advanced bio-inspired photoplethysmography (PPG) and ECG pattern recognition system for medical assessment. Sensors 18, 405 (2018). https://doi.org/10.3390/s18020405

    Article  Google Scholar 

  25. Rustand, Å.: Ambient-light photoplethysmography. Doctoral dissertation, master’s thesis, Norwegian University of Science and Technology, Department of Electronics and Telecommunications (2012)

    Google Scholar 

  26. Shelley, K., Shelley, S.: Pulse oximeter waveform: photoelectric plethysmography. Clin. Monit. 420–428 (2001). https://www.researchgate.net/profile/Kirk_Shelley/publication/224765089_Pulse_Oximeter_Waveform_Photoelectric_Plethysmography/links/0c960529365c4977a4000000.pdf. Carol Lake, R Hines, and C Blitt, Eds: WB Saunders Company

  27. Shi, J., Tomasi, C.: Good features to track. In: CVPR 1994, Seatle (1994)

    Google Scholar 

  28. Sun, Y., Papin, C., Azorin-Peris, V., Kalawsky, R., Greenwald, S., Hu, S.: Use of ambient light in remote photoplethysmographic systems: comparison between a high-performance camera and a low-cost webcam. J. Biomed. Optics 17(3), 037005 (2012). https://doi.org/10.1117/1.JBO.17.3.037005

    Article  Google Scholar 

  29. Teplov, V., Nippolainen, E., Makarenko, A.A., Giniatullin, R., Kamshilin, A.A.: Ambiguity of mapping the relative phase of blood pulsations. Biomed. Opt. Express 5(9), 3123–39 (2014). https://doi.org/10.1364/BOE.5.003123

    Article  Google Scholar 

  30. Tomasi, C., Kanade, T.: Detection and tracking of point features. Technical report, CMU-CS-91-132 (1991)

    Google Scholar 

  31. Tulyakov, S., Alameda-pineda, X., Ricci, E., Yin, L., Cohn, J.F., Sebe, N., Sommarive, V., Kessler, F.B., Sommarive, V.: Self-adaptive matrix completion for heart rate estimation from face videos under realistic conditions. In: CVPR, pp. 2396–2404 (2016)

    Google Scholar 

  32. Verkruysse, W., Svaasand, L.O., Nelson, J.S.: Remote plethysmographic imaging using ambient light. Opt. Express 16(26), 21434–21445 (2008). https://doi.org/10.1364/OE.16.021434

    Article  Google Scholar 

  33. Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. CVPR 1, 511–518 (2001)

    Google Scholar 

  34. Wang, W., Stuijk, S., Haan, G.D.: A novel algorithm for remote photoplethysmography : spatial subspace rotation. IEEE Trans. Biomed. Eng. 63(9), 1974–1984 (2016)

    Article  Google Scholar 

  35. Wieringa, F.P., Mastik, F., Van Der Steen, A.F.W.: Contactless multiple wavelength photoplethysmographic imaging: a first step toward “spO 2 camera” technology. Ann. Biomed. Eng. 33(8), 1034–1041 (2005). https://doi.org/10.1007/s10439-005-5763-2

    Article  Google Scholar 

  36. Wu, H.Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., Freeman, W.: Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph. 31(4), 1–8 (2012). https://doi.org/10.1145/2185520.2335416

    Article  Google Scholar 

  37. Zaproudina, N., Teplov, V., Nippolainen, E., Lipponen, J.A., Kamshilin, A.A., Närhi, M., Karjalainen, P.A., Giniatullin, R.: Asynchronicity of facial blood perfusion in migraine. PLoS ONE 8(12) (2013). https://doi.org/10.1371/journal.pone.0080189

    Article  Google Scholar 

  38. Zaunseder, S., Trumpp, A., Wedekind, D., Malberg, H.: Cardiovascular assessmentby imaging photoplethysmography - a review. Biomed. Eng./Biomedizinische Technik 63 (2018). https://doi.org/10.1515/bmt-2017-0119

    Article  Google Scholar 

Download references

Acknowledgments

The authors want to acknowledge the director of supporting project, Dmitry Shaposhnikov, a leading researcher at the Center of Neurotechnologies.

Funding

The work is supported by the Russian Ministry for Education and Science, project no. 2.955.2017/4.6 “Development of the hardware and software system for monitoring the attention level and psychoemotional state of pilots and dispatching personnel to improve flight safety”.

Author information

Authors and Affiliations

  1. I.I. Vorovich Institute for Mathematics, Mechanics and Computer Science, Southern Federal University, Rostov-on-Don, Russian Federation

    Mikhail Kopeliovich

  2. Center of Neurotechnologies, Southern Federal University, Rostov-on-Don, Russian Federation

    Mikhail Petrushan

Authors
  1. Mikhail Kopeliovich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mikhail Petrushan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mikhail Kopeliovich .

Editor information

Editors and Affiliations

  1. School of Computing, Computer Science Research Institute, Ulster University, Newtownabbey, UK

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Rahul Bhatia

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, UK

    Supriya Kapoor

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

Kopeliovich, M., Petrushan, M. (2020). Color Signal Processing Methods for Webcam-Based Heart Rate Evaluation. In: Bi, Y., Bhatia, R., Kapoor, S. (eds) Intelligent Systems and Applications. IntelliSys 2019. Advances in Intelligent Systems and Computing, vol 1038. Springer, Cham. https://doi.org/10.1007/978-3-030-29513-4_53

Download citation

Publish with us

Policies and ethics

Search

Navigation