Automatic Webcam-Based Human Heart Rate Measurements Using Laplacian Eigenmap

  • Lan Wei
  • Yonghong Tian
  • Yaowei Wang
  • Touradj Ebrahimi
  • Tiejun Huang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7725)


Non-contact, long-term monitoring human heart rate is of great importance to home health care. Recent studies show that Photoplethysmography (PPG) can provide a means of heart rate measurement by detecting blood volume pulse (BVP) in human face. However, most of existing methods use linear analysis method to uncover the underlying BVP, which may be not quite adequate for physiological signals. They also lack rigorous mathematical and physiological models for the subsequent heart rate calculation. In this paper, we present a novel webcam-based heart rate measurement method using Laplacian Eigenmap (LE). Usually, the webcam captures the PPG signal mixed with other sources of fluctuations in light. Thus exactly separating the PPG signal from the collected data is crucial for heart rate measurement. In our method, more accurate BVP can be extracted by applying LE to efficiently discover the embedding ties of PPG with the nonlinear mixed data. We also operate effective data filtering on BVP and get heart rate based on the calculation of interbeat intervals (IBIs). Experimental results show that LE obtains higher degrees of agreement with measurements using finger blood oximetry than Independent Component Analysis (ICA), Principal Component Analysis (PCA) and other five alternative methods. Moreover, filtering and processing on IBIs are proved to increase the measuring accuracy in experiments.


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  1. 1.
    Poh, M.Z., McDuff, D.J., Picard, R.W.: Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt. Express, 10762–10774 (2010)Google Scholar
  2. 2.
    Lewandowska, M., Rumiski, J., Kocejko, T.: Measuring pulse rate with a webcam - a non-contact method for evaluating cardiac activity. In: Computer Science and Information Systems (FedCSIS), pp. 18–21 (2011)Google Scholar
  3. 3.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps for dimensionality reduction and data representation. Neural Computation 15, 1373–1396 (2003)MATHCrossRefGoogle Scholar
  4. 4.
    Shlens, J.: A tutorial on principal component analysis. Institute for Nonlinear Science, UCSD (2005)Google Scholar
  5. 5.
    Mika, S., Ratsch, G., Weston, J., Scholkopf, B., Mullers, K.R.: Fisher discriminant analysis with kernels. Neural Networks for Signal Processing IX, 41–48Google Scholar
  6. 6.
    Comon, P.: Independent component analysis: a new concept? Signal Processing 36, 287–314 (1994)MATHCrossRefGoogle Scholar
  7. 7.
    Tenenbaum, J., De Silva, V., Langofrd, J.: A global geomertic framework for nonlinear dimension reduction. Science, 2319–2323 (2000)Google Scholar
  8. 8.
    Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 5500, 2323–2326 (2000)CrossRefGoogle Scholar
  9. 9.
    Zhang, Z., Zha, H.: Principal mainfolds and nonlinear dimensionality reduction by local tangent space alignment. SIAM Journal Scientific Computing, 313–338 (2004)Google Scholar
  10. 10.
    Weinberger, K., Saul, L.: Unsupervised learning of image manifolds by semidefinite programming. Int. J. Comp. Vision, 11–90 (2006)Google Scholar
  11. 11.
    He, X., Niyogi, P.: Locality preserving projections. In: Proc. Conf. Advances in Neural Information Processing Systems (2003)Google Scholar
  12. 12.
    Challoner, A.V.J.: Photoelectric plethysmography for estimating cutaneous blood flow non-invasive physiological measurements, vol. 125. Academic Press (1979)Google Scholar
  13. 13.
    Webster, J.G.: Design of pulse oximeters. Institute of Physics Publishing, Bristol (1997)CrossRefGoogle Scholar
  14. 14.
    Nakajima, K., Tamura, T., Miike, H.: Monitoring of heart and respiratory rates by photoplethysmography using a digital filtering technique. Med. Eng. Phys., 365–372 (1996)Google Scholar
  15. 15.
    Johansson, A., Oberg, P.A.: Estimation of respiratory volumes from the photoplethysmographic signal. Med. Biol. Eng. Comput., 42–47 (1999)Google Scholar
  16. 16.
    Aoyagi, T., Miyasaka, K.: Pulse oximetry: its invention, contribution to medicine, and future tasks. Anesth. Analg., S1–S3 (2002)Google Scholar
  17. 17.
    Naschitz, J.E., et al.: Pulse transit time by r-wave-gated infrared photoplethysmography: review of the literature and personal experience. Clin. Monit. Comput., 333–342 (2004)Google Scholar
  18. 18.
    Garbey, M., Merla, A., Pavlidis, I.: Estimation of blood flow speed and vessel location from thermal video. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 356–363 (2004)Google Scholar
  19. 19.
    Sun, N., Garbey, M., Merla, A.: I Pavlidis: Imaging the cardiovascular pulse. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition (2005)Google Scholar
  20. 20.
    Pavlidis, I., Dowdall, J., Sun, N., Puri, C., Fei, J., Garbey, M.: Interacting with human physiology. Comput., Vis., Image Underst. 108 (2007)Google Scholar
  21. 21.
    Fei, Pavlidis, I.: Thermistor at a distance: unobtrusive measurement of breathing. IEEE Trans. Biomed. Eng. 57, 988–998 (2010)CrossRefGoogle Scholar
  22. 22.
    Zheng, J., Hu, S., Azorin-Peris, V., Echiadi, A.: Remote simultaneous dual wavelength imaging photoplethysmography: a further step towards 3-d mapping of skin blood microcirculation. In: Proc. of SPIE, vol. 206, pp. 159–178 (2008)Google Scholar
  23. 23.
    Jianchu, Y., Warren, S.: A short study to assess the potential of independent component analysis for motion artifact separation in wearable pulse oximeter signals. In: IEEE Conference of the Engineering in Medicine and Biology Society, pp. 3585–3588 (2005)Google Scholar
  24. 24.
    Poh, M.Z., McDuff, D.J., Picard, R.W.: Advancements in noncontact, multi-parameter physiological measurements using a webcam. IEEE Trans. Biomed. Eng. (2011)Google Scholar
  25. 25.
    Belkin, M., Niyogi, P.: Laplacian eigenmaps and spectral techniques for embedding and clustering, pp. 585–591. MIT Press, MA (2001)Google Scholar
  26. 26.
    Viola, P., Jones, M.: Rapid object detection using a boosted cascade of simple features. In: Proceedings of the International Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 1063–6919 (2001)Google Scholar
  27. 27.
    Lienhart, R., Maydt, J.: An extended set of haar-like features for rapid object detection. In: Proceedings of the IEEE Conference on Image Processing, pp. 900–903 (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Lan Wei
    • 1
  • Yonghong Tian
    • 1
  • Yaowei Wang
    • 2
  • Touradj Ebrahimi
    • 3
  • Tiejun Huang
    • 1
  1. 1.National Engineering Lab for Video TechnologyPeking UniversityChina
  2. 2.Department of Electronics EngineeringBeijing Institute of TechnologyChina
  3. 3.Swiss Federal Institute of TechnologySwitzerland

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