Advertisement

Cluster Computing

, Volume 22, Supplement 4, pp 8199–8206 | Cite as

Non-contact detection of human heart rate with Kinect

  • Liqian ZhouEmail author
  • Ming Yin
  • Xi Xu
  • Xinpan Yuan
  • Xiaojun Liu
Article
  • 198 Downloads

Abstract

Non-contact detection of heart rate has been addressed by researchers from very different fields. However, the low accuracy of measuring results in the difficulties in methodology deployment. This paper introduces the principle of heartbeat detection. A detection scheme by using Kinect is proposed. Further, the signal processing approach based on JADE algorithm is developed to efficiently remove the clutter in mixture signals, and it enables accurate transforming via Z-score normalization. Due to the significances presented in this work, the detection error is 1.79%, when processed with the proposed algorithm. Experimental results are statistically analyzed, which makes it a promising basis for the realization of heart rate detection.

Keywords

Kinect Heart rate JADE algorithm Non-contact detection 

Notes

Acknowledgements

Financial support was provided by National Natural Science Foundation of China (61402165), Hunan Provincial Natural Science Foundation of China (2016JJ5036 and 2015JJ3058), Key Scientific Research Fund of Hunan Provincial Education Department in China (17A052), and Aid program for Science and Technology Innovative Research Team in High Educational Institutions of Hunan Province.

References

  1. 1.
    Chatlapalli, S.M.: An integrated signal processing environment for detection of sleep disordered breathing in children using spectral and nonlinear dynamic measures of heart rate variability signal, Master Dissertation. The University of Texas at El Paso, El Paso (2005)Google Scholar
  2. 2.
    Billman, G.E.: Heart rate variability—a historical perspective. Front. Physiol. 2, 86 (2011)CrossRefGoogle Scholar
  3. 3.
    Gan, K.B., Zahedi, E., Ali, M.A.: Transabdominal fetal heart rate detection using NIR photopleythysmography: instrumentation and clinical results. IEEE Trans. Biomed. Eng. 56(8), 2075–2082 (2009)CrossRefGoogle Scholar
  4. 4.
    Freeman, R.K., Garite, T.J., Nageotte, M.P.: Fetal Heart Rate Monitoring, 3rd edn. Williams & Wilkins, Philadelphia (2003)Google Scholar
  5. 5.
    Cho, H.S., Park, Y.J., Lyu, H.K., Cho, J.H.: Novel heart rate detection method using UWB impulse radar. J. Signal Process. Syst. 87, 229–239 (2017)CrossRefGoogle Scholar
  6. 6.
    Bilich, C.G.: Bio-Medical sensing using ultra wideband communications and radar technology: a feasibility study. In: Proceedings of the Pervasive Health Conference and Workshops, pp. 1–9 (2009)Google Scholar
  7. 7.
    Sandham, W., Hamilton, D., Laguna, P., Cohen, M.: Advances in electrocardiogram signal processing and analysis. EURASIP J. Adv. Signal Process. 2007(1), 105 (2007)CrossRefGoogle Scholar
  8. 8.
    Liu, B., Li, J., Chen, C., Tan, W., Chen, Q., Zhou, M.: Efficient motif-discovery for large-scale time series in healthcare. IEEE Trans. Ind. Inform. 11(3), 583–590 (2015)CrossRefGoogle Scholar
  9. 9.
    Fan, X., Chen, R., He, C., Cai, Y., Wang, P., Li, Y.: Toward automated analysis of electrocardiogram big data by graphics processing unit for mobile health application. IEEE Access 5, 17136–17148 (2017)CrossRefGoogle Scholar
  10. 10.
    Thakor, N.V., Zhu, Y.-S.: Applications of adaptive filtering to ECG analysis: noise cancellation and arrhythmia detection. IEEE Trans. Bio-med. Eng. 38(8), 785–794 (1991)CrossRefGoogle Scholar
  11. 11.
    Moody, G.B., Mark, R.G.: Development and evaluation of a two-lead ECG analysis program. Comput. Cardiol. 9, 39–44 (1982)Google Scholar
  12. 12.
    Zhang, Q., Zhou, D., Zeng, X.: A novel framework for motion-tolerant instantaneous heart rate estimation by phase-domain multiview dynamic time warping. IEEE Trans. Biomed. Eng. 64(11), 2562–2574 (2017)CrossRefGoogle Scholar
  13. 13.
    Shastri, D.: Imaging facial signs of neurophysiological responses. IEEE Trans. Biomed. Eng. 56(2), 477–484 (2009)CrossRefGoogle Scholar
  14. 14.
    Garbey, M.: Contact-free measurement of cardiac pulse based on the analysis of thermal imagery. IEEE Trans. Biomed. Eng. 54(8), 1418–1426 (2007)CrossRefGoogle Scholar
  15. 15.
    Holdsworth, D.: Characterization of common carotid artery blood-flow waveforms in normal human subjects. Physiol. Meas. 20(3), 219–220 (1999)CrossRefGoogle Scholar
  16. 16.
    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(10), 10762–10774 (2010)CrossRefGoogle Scholar
  17. 17.
    Poh, M.Z., McDuff, D.J., Picard, RW.: A medical mirror for non-contact health monitoring. In: ACM SIGGRAPH 2011 Emerging Technologies, p. (2011)Google Scholar
  18. 18.
    González-Landaeta, R., Casas, O., Pallàs-Areny, R.: Heart rate detection from plantar bioimpedance measurements. IEEE Trans. Biomed. Eng. 55(3), 1163–1167 (2008)CrossRefGoogle Scholar
  19. 19.
    Wu, H.Y., Rubinstein, M., Shih, E., et al.: Eulerian video magnification for revealing subtle changes in the world. ACM Trans. Graph. 31(4), 13–15 (2012)CrossRefGoogle Scholar
  20. 20.
    Kim, S.W., Choi, S.B., An, Y.J., Kim, B.H., Kim, D.W., Yook, J.G.: Heart rate detection during sleep using a flexible RF resonator and injection-locked PLL sensor. IEEE Trans. Biomed. Eng. 62(11), 2568–2575 (2015)CrossRefGoogle Scholar
  21. 21.
    He, Q., Wang, Y.: Research on system of facial expression capture and animation simulation based on kinect. J. Graph. 37(3), 290–295 (2016)Google Scholar
  22. 22.
    Qu, C., Sun, J., Wang, J., Zhu, X.: Automatic fall detection for the elderly using kinect sensor. Chin. J. Sens. Actuators 29(3), 013 (2016)Google Scholar
  23. 23.
    Shen, S., Gao, F., Xu, N.: The game of virtual reality head rehabilitation based on Kinect. J. Syst. Simul. 28(8), 1904–1908 (2016)Google Scholar
  24. 24.
    Ma, S., Zhou, C., Zhang, L., Hong, W.: Twist-lock online recognition based on improved incremental PCA by Kinect. J. Jilin Univ. 46(3), 890–896 (2016)Google Scholar
  25. 25.
    Kraus, U., Schneider, A., Breitner, S., Hampel, R., Rükerl, R., Pitz, M., Geruschkat, U., Belcredi, P., Radon, K., Peters, A.: Individual daytime noise exposure during routine activities and heart rate variability in adults: a repeated measures study. Environ. Health Perspect. 121, 607–612 (2013)CrossRefGoogle Scholar
  26. 26.
    Allen, J.: Photoplethysmography and its application in clinical physiological measurement. Physiol. Meas. 28(3), 1–39 (2007)CrossRefGoogle Scholar
  27. 27.
    Hattay, J., Belaid, S., Lebrun, D., Naanaa, W.: Digital in-line particle holography: twin-image suppression using sparse blind source separation. Signal Image Video Process 9(8), 1767–1774 (2015)CrossRefGoogle Scholar
  28. 28.
    Yin, P., Sun, Y., Xin, J.: A geometric blind source separation method based on facet component analysis. Signal Image Video Process 10(1), 19–28 (2016)CrossRefGoogle Scholar
  29. 29.
    Mowla, M.R., Ng, S.C., Zilany, M.S., Paramesran, R.: Artifactsmatched blind source separation and wavelet transform for multichannel EEG denoising. Biomed. Signal Process. Control 22, 111–118 (2015)CrossRefGoogle Scholar
  30. 30.
    Badawi, W.K.M., Chibelushi, C.C., Patwary, M.N., Moniri, M.: Specular-based illumination estimation using blind signal separation techniques. IET Image Process. 6(8), 1181–1191 (2012)MathSciNetCrossRefGoogle Scholar
  31. 31.
    Nordhausen, K., Cardoso, J.F., Miettinen, J., et al.: JADE and other BSS methods as well as some BSS performance criteria. R package version 1.1-0 (2012)Google Scholar
  32. 32.
    Zhou, X., Li, K., Zhou, Y., Li, K.: Adaptive processing for distributed skyline queries over uncertain data. IEEE Trans. Knowl. Data Eng. 28(2), 371–384 (2016)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of ComputerHunan University of TechnologyZhuzhouChina

Personalised recommendations