Nothing Special   »   [go: up one dir, main page]

Skip to main content

Assessing the Feasibility of Remote Photoplethysmography Through Videocalls: A Study of Network and Computing Constraints

  • Conference paper
  • First Online:
Image Analysis (SCIA 2023)

Abstract

Remote photoplethysmography (rPPG) is a promising non-invasive technique for measuring vital signs remotely, such as through videocalls. However, network and computing constraints can significantly compromise its accuracy. In this study, we evaluated the effects of these constraints on rPPG methods using four reference datasets and a standard unsupervised rPPG signal extraction pipeline. Our experiments simulated the impact of frame dropping, streaming video at different resolutions and frame rates, and other resource limitations. We found that these constraints can significantly degrade rPPG accuracy, but implementing specific strategies (such as reconstructing the signal in the receiver) can mitigate these effects. For example, with a 20% of frame loss, our proposed strategies reduced the MAE increase from 539% to 29%. These findings highlight the importance of considering network and computing constraints in rPPG applications.

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

Access this chapter

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
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

Similar content being viewed by others

References

  1. Blackford, E., Estepp, J.: Effects of frame rate and image resolution on pulse rate measured using multiple camera imaging photoplethysmography. Prog. Biomed. Opti. Imaging Proc. SPIE 9417 (2015). https://doi.org/10.1117/12.2083940

  2. Bobbia, S., Macwan, R., Benezeth, Y., Mansouri, A., Dubois, J.: Unsupervised skin tissue segmentation for remote photoplethysmography. Pattern Recogn. Lett. 124, 82–90 (2019). https://doi.org/10.1016/j.patrec.2017.10.017, https://www.sciencedirect.com/science/article/pii/S0167865517303860

  3. Boccignone, G., et al.: pyVHR: a Python framework for remote photoplethysmography. PeerJ Comput. Sci. 8, e929 (2022)

    Article  Google Scholar 

  4. Botina-Monsalve, D., et al.: Long short-term memory deep-filter in remote photoplethysmography. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1242–1249, June 2020. https://doi.org/10.1109/CVPRW50498.2020.00161

  5. Álvarez Casado, C., Bordallo López, M.: Face2PPG: an unsupervised pipeline for blood volume pulse extraction from faces (2022). https://doi.org/10.48550/ARXIV.2202.04101, https://arxiv.org/abs/2202.04101

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

    Article  Google Scholar 

  7. Hanfland, S., Paul, M.: Video format dependency of PPGI signals. In: Proceedings of the International Conference on Electrical Engineering, vol. 1, p. 2 (2016)

    Google Scholar 

  8. Heusch, G., Anjos, A., Marcel, S.: A reproducible study on remote heart rate measurement. CoRR abs/1709.00962 (2017). http://arxiv.org/abs/1709.00962

  9. Khanam, F.T.Z., Al-Naji, A., Chahl, J.: Remote monitoring of vital signs in diverse non-clinical and clinical scenarios using computer vision systems: a review. Appl. Sci. 9(20), 4474 (2019). https://doi.org/10.3390/app9204474, https://www.mdpi.com/2076-3417/9/20/4474

  10. Kumar, M., Suliburk, J., Veeraraghavan, A., Sabharwal, A.: PulseCam: a camera-based, motion-robust and highly sensitive blood perfusion imaging modality. Sci. Rep. 10, 4825 (2020). https://doi.org/10.1038/s41598-020-61576-0

  11. Kumar, M., Veeraraghavan, A., Sabharwal, A.: DistancePPG: robust non-contact vital signs monitoring using a camera. Biomed. Opt. Express 6, 1565–1588 (2015). https://doi.org/10.1364/BOE.6.001565

  12. Lampier, L., Floriano, A., Delisle Rodriguez, D., Caldeira, E., Bastos-Filho, T.: Effect of image resolution on remote photoplethysmography: towards emotion detection in children with autism spectrum disorder, October 2019. https://doi.org/10.17648/sbai-2019-111242

  13. Lewandowska, M., Rumiński, J., Kocejko, T., Nowak, J.: Measuring pulse rate with a webcam - a non-contact method for evaluating cardiac activity. In: Federated Conference on Computer Science and Information Systems (FedCSIS), pp. 405–410, September 2011

    Google Scholar 

  14. Li, X., Chen, J., Zhao, G., Pietikäinen, M.: Remote heart rate measurement from face videos under realistic situations. In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. 4264–4271, June 2014. https://doi.org/10.1109/CVPR.2014.543

  15. Maity, A.K., Wang, J., Sabharwal, A., Nayar, S.K.: RobustPPG: camera-based robust heart rate estimation using motion cancellation. Biomed. Opt. Express 13(10), 5447–5467 (2022). https://doi.org/10.1364/BOE.465143, https://opg.optica.org/boe/abstract.cfm?URI=boe-13-10-5447

  16. McDuff, D., Blackford, E., Estepp, J.: The impact of video compression on remote cardiac pulse measurement using imaging photoplethysmography, pp. 63–70, May 2017. https://doi.org/10.1109/FG.2017.17

  17. Narayanan, A., et al.: A variegated look at 5G in the wild: performance, power, and QoE implications. In: Proceedings of the 2021 ACM SIGCOMM 2021 Conference, SIGCOMM 2021, pp. 610–625. Association for Computing Machinery, New York, NY, USA (2021). https://doi.org/10.1145/3452296.3472923, https://doi.org/10.1145/3452296.3472923

  18. Nowara, E.M., McDuff, D., Veeraraghavan, A.: The benefit of distraction: denoising camera-based physiological measurements using inverse attention. In: 2021 IEEE/CVF International Conference on Computer Vision (ICCV), pp. 4935–4944, October 2021. https://doi.org/10.1109/ICCV48922.2021.00491

  19. Pilz, C.S., Zaunseder, S., Krajewski, J., Blazek, V.: Local group invariance for heart rate estimation from face videos in the wild. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1335–13358, June 2018. https://doi.org/10.1109/CVPRW.2018.00172

  20. 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 

  21. Premkumar, S., Hemanth, D.J.: Intelligent remote photoplethysmography-based methods for heart rate estimation from face videos: a survey. Informatics 9(3), 57 (2022). https://doi.org/10.3390/informatics9030057, https://www.mdpi.com/2227-9709/9/3/57

  22. Rapczynski, M., Werner, P., Al-Hamadi, A.: Effects of video encoding on camera based heart rate estimation. IEEE Trans. Biomed. Eng. 66, 3360–3370 (2019). https://doi.org/10.1109/TBME.2019.2904326

  23. Sun, Y., Hu, S., Azorin Peris, V., Kalawsky, R., Greenwald, S.: Noncontact imaging photoplethysmography to effectively access pulse rate variability. J. Biomed. Opt. 18, 61205 (2013). https://doi.org/10.1117/1.JBO.18.6.061205

  24. Sun, Y., Thakor, N.: Photoplethysmography revisited: from contact to noncontact, from point to imaging. IEEE Trans. Biomed. Eng. 63, 463–477 (2015). https://doi.org/10.1109/TBME.2015.2476337

  25. 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, http://www.osapublishing.org/oe/abstract.cfm?URI=oe-16-26-21434

  26. Wang, W., den Brinker, A., Haan, G.: Discriminative signatures for remote-PPG. IEEE Trans. Biomed. Eng. 67, 1462–1473 (2019). https://doi.org/10.1109/TBME.2019.2938564

  27. Wang, W., den Brinker, A.C., Stuijk, S., de Haan, G.: Algorithmic principles of remote PPG. IEEE Trans. Biomed. Eng. 64(7), 1479–1491 (2017). https://doi.org/10.1109/TBME.2016.2609282

    Article  Google Scholar 

  28. Yu, Z., Peng, W., Li, X., Hong, X., Zhao, G.: Remote heart rate measurement from highly compressed facial videos: an end-to-end deep learning solution with video enhancement. In: IEEE/CVF International Conference on Computer Vision (ICCV), pp. 151–160, October 2019. https://doi.org/10.1109/ICCV.2019.00024

  29. Zhao, C., Chen, W., Lin, C.L., Wu, X.: Physiological signal preserving video compression for remote photoplethysmography. IEEE Sens. J. 19(12), 4537–4548 (2019). https://doi.org/10.1109/JSEN.2019.2899102

    Article  Google Scholar 

  30. Zhao, C., Lin, C.L., Chen, W., Li, Z.: A novel framework for remote photoplethysmography pulse extraction on compressed videos. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp. 1380–138009, June 2018. https://doi.org/10.1109/CVPRW.2018.00177

Download references

Acknowledgments

This research has been supported by the Academy of Finland 6G Flagship program under Grant 346208 and PROFI5 HiDyn under Grant 326291.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Constantino Álvarez Casado .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Álvarez Casado, C., Nguyen, L., Silvén, O., Bordallo López, M. (2023). Assessing the Feasibility of Remote Photoplethysmography Through Videocalls: A Study of Network and Computing Constraints. In: Gade, R., Felsberg, M., Kämäräinen, JK. (eds) Image Analysis. SCIA 2023. Lecture Notes in Computer Science, vol 13886. Springer, Cham. https://doi.org/10.1007/978-3-031-31438-4_38

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-31438-4_38

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-31437-7

  • Online ISBN: 978-3-031-31438-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics