Journal of Medical and Biological Engineering

, Volume 37, Issue 6, pp 867–878 | Cite as

A Motion Heart-Rate Monitor Watch with Improved Grey Differential Equation Model Based on Reflective Photoplethysmography

  • Xiru Li
  • Xiaofeng Li
  • Haibo Tan
  • Jinlin Xu
  • Munan Yuan
Original Article


With the advantage of noninvasive measurement, reflective photoplethysmography (PPG) has been adopted by a group of gadgets to estimate heart rate (HR) from the wrists. Challenges include PPG artifacts reduction and comfortable type design. Aimed at improving the accuracy of measurement during wrist movements such as handshake or exercises, in this paper, a motion HR monitor watch has presented. The work mainly focused on two categories: the HR extraction algorithm derived from grey differential equation model (abbreviated as GM(1, 1) model) combined with acceleration model and the enhanced hardware structure designed with 523 nm green leds. The tests have been carried out and results analysis show high correlation among the values measured respectively from this watch, HR chest belt (taken as HR gold standard) and commercial motion HR watch in various intensities of exercises.


Grey differential equation model (abbreviated as GM(1, 1) model) Heart rate (HR) Photoplethysmography (PPG) Artifacts reduction HR watch 



We thank the volunteers from Hefei Institutes of Physical Science, Chinese Academy of Sciences take part in experiments.


National Science & Technology Pillar Program of China (No. 2013BAH14F01); National Natural Science Foundation of China (No. 61301059).


  1. 1.
    Fox, K., Borer, J. S., Camm, A. J., Danchin, N., Ferrari, R., Sendon, J. L. L., et al. (2007). Resting heart rate in cardiovascular disease. Journal of the American College of Cardiology, 50(9), 823–830.CrossRefGoogle Scholar
  2. 2.
    Hertzman, A. B., & Spealman, C. R. (1937). Observations on the finger volume pulse recorded photoelectrically. American Journal of Physiology, 119, 334–335.Google Scholar
  3. 3.
    Cennini, G., Arguel, J., Akşit, K., & Van, L. A. (2010). Heart rate monitoring via remote photoplethysmography with motion artifacts reduction. Optics Express, 18(5), 4867–4875.CrossRefGoogle Scholar
  4. 4.
    Kong, L., Zhao, Y., Dong, L., Jian, Y., Jin, X., Li, B., et al. (2013). Non-contact detection of oxygen saturation based on visible light imaging device using ambient light. Optics Express, 21(15), 17464–17471.CrossRefGoogle Scholar
  5. 5.
    Yoon, Y. Z., & Yoon, G. W. (2006). Nonconstrained blood pressure measurement by photoplethysmography. Journal of the Optical Society of Korea, 10(2), 91–95.CrossRefGoogle Scholar
  6. 6.
    Wang, C., Li, Z., & Wei, X. (2013). Monitoring heart and respiratory rates at radial artery based on ppg. Optik—International Journal for Light and Electron Optics, 124(19), 3954–3956.CrossRefGoogle Scholar
  7. 7.
    Perdue, K. L., Westerlund, A., & Mccormick, S. A. (2014). Extraction of heart rate from functional near-infrared spectroscopy in infants. Journal of Biomedical Optics, 19(6), 067010.CrossRefGoogle Scholar
  8. 8.
    Trajkovic, I., Scholkmann, F., & Wolf, M. (2011). Estimating and validating the interbeat intervals of the heart using near-infrared spectroscopy on the human forehead. Journal of Biomedical Optics, 16(8), 579–595.CrossRefGoogle Scholar
  9. 9.
    Lee, B., Lee, B., & Chung, W. (2015). Wristband-type driver vigilance monitoring system using smartwatch. IEEE Sensors Journal, 15(10), 5624–5633.CrossRefGoogle Scholar
  10. 10.
    Alian, A. A., & Shelley, K. H. (2014). Photoplethysmography. Best Practice & Research Clinical Anaesthesiology, 28(4), 395–406.CrossRefGoogle Scholar
  11. 11.
    Zheng, D., & Murray, A. (2009). Non-invasive quantification of peripheral arterial volume distensibility and its non-linear relationship with arterial pressure. Journal of Biomechanics, 42(8), 1032–1037.CrossRefGoogle Scholar
  12. 12.
    Nieveen, J., Van de, S. L. B., & Reichert, W. J. (1956). Photoelectric plethysmography using reflected light. Cardiology, 29(3), 160–173.CrossRefGoogle Scholar
  13. 13.
    Weinman, J., Hayat, A., & Raviv, G. (1977). Reflection photoplethysmography of arterial-blood-volume pulses. Medical & Biological Engineering & Computing, 15(1), 22–31.CrossRefGoogle Scholar
  14. 14.
    Webster, J. (1997). Design of pulse oximeters. Bristol: Institute of Physics Pub.Google Scholar
  15. 15.
    Spigulis, J., Gailite, L., Lihachev, A., & Erts, R. (2007). Simultaneous recording of skin blood pulsations at different vascular depths by multiwavelength photoplethysmography. Applied Optics, 46(10), 1754–1759.CrossRefGoogle Scholar
  16. 16.
    Kamshilin, A. A., Mamontov, O. V., Koval, V. T., Zayats, G. A., & Romashko, R. V. (2015). Influence of a skin status on the light interaction with dermis. Biomedical Optics Express, 6(11), 4326–4334.CrossRefGoogle Scholar
  17. 17.
    Kamshilin, A. A., Nippolainen, E., Sidorov, I. S., Vasilev, P. V., Erofeev, N. P., & Podolian, N. P. (2015). A new look at the essence of the imaging photoplethysmography. Scientific Reports, 5(5), 10494.CrossRefGoogle Scholar
  18. 18.
    Severinghaus, J. W., & Kelleher, J. F. (1992). Recent developments in pulse oximetry. Anesthesiology, 76, 1018–1038.CrossRefGoogle Scholar
  19. 19.
    Trivedi, N. S., Ghouri, A. F., Shah, N. K., Lai, E., & Barker, S. J. (1997). Effects of motion, ambient light, and hypoperfusion on pulse oximeter function. Journal of Clinical Anesthesia, 9(3), 179–183.CrossRefGoogle Scholar
  20. 20.
    Petterson, M. T., Begnoche, V. L., & Graybeal, J. M. (2007). The effect of motion on pulse oximetry and its clinical significance. Anesthesia and Analgesia, 105, S78–S84.CrossRefGoogle Scholar
  21. 21.
    Hayes, M. J., & Smith, P. R. (1998). Artifact reduction in photoplethysmography. Applied Optics, 37(31), 7437–7446.CrossRefGoogle Scholar
  22. 22.
    Wijshoff, R. W., Mischi, M., Veen, J., Am, V. D. L., & Aarts, R. M. (2012). Reducing motion artifacts in photoplethysmograms by using relative sensor motion: phantom study. Journal of Biomedical Optics, 17(17), 345–352.Google Scholar
  23. 23.
    Kim, B. S., & Yoo, S. K. (2006). Motion artifact reduction in photoplethysmography using independent component analysis. IEEE Transactions on Biomedical Engineering, 53(3), 566–568.MathSciNetCrossRefGoogle Scholar
  24. 24.
    Sun, Y., Papin, C., Azorin-Peris, V., Kalawsky, R., Greenwald, S., & Hu, S. (2012). Use of ambient light in remote photoplethysmographic systems: comparison between a high-performance camera and a low-cost webcam. Journal of Biomedical Optics. doi: 10.1117/1.JBO.22.5.056003.CrossRefGoogle Scholar
  25. 25.
    Lai, P. H., & Kim, I. (2015). Lightweight wrist photoplethysmography for heavy exercise: motion robust heart rate monitoring algorithm. Healthcare Technology Letters, 2(1), 6–11.CrossRefGoogle Scholar
  26. 26.
    López-Silva, S. M., Giannetti, R., Dotor, M. L., Silveira, J. P., Golmayo, D., Miguel-Tobal, F., et al. (2012). Heuristic algorithm for photoplethysmographic heart rate tracking during maximal exercise test. Journal of Medical & Biological Engineering, 32(3), 181–188.CrossRefGoogle Scholar
  27. 27.
    Matsumura, K., Rolfe, P., Lee, J., & Yamakoshi, T. (2014). iphone 4 s photoplethysmography: which light color yields the most accurate heart rate and normalized pulse volume using the iphysiometer application in the presence of motion artifact. PLoS ONE, 9(9), 360–367.Google Scholar
  28. 28.
    Kim, I., Lai, P., Lobo, R., & Gluckman, B. J. (2014). Challenges in wearable personal health monitoring systems. In: International conference of the IEEE engineering in medicine and biology society, pp. 5264–5267.Google Scholar
  29. 29.
    Beiderman, Y., Talyosef, R., Yeori, D., Garcia, J., Mico, V., & Zalevsky, Z. (2012). Use of pc mouse components for continuous measuring of human heartbeat. Applied Optics, 51(16), 3323–3328.CrossRefGoogle Scholar
  30. 30.
    Deng, J. L. (1982). The controls problems of grey systems. Systems & Control Letters, 1, 288–294.MathSciNetCrossRefGoogle Scholar
  31. 31.
    Huang, Y. O. P., & Wang, S. F. (2007). The identification of fuzzy grey prediction system by genetic algorithms. International Journal of Systems Science, 28, 15–24.CrossRefGoogle Scholar
  32. 32.
    Murray, F. T., Ringwood, J. V., & Austin, P. C. (2000). Integration of multi-time-scale models in time series forecasting. International Journal of Systems Science, 31, 1249–1260.CrossRefGoogle Scholar
  33. 33.
    Yu, Z., Yang, C., Zhang, Z., & Jiao, J. (2015). Error correction method based on data transformational GM(1, 1) and application on tax forecasting. Applied Soft Computing, 37, 554–560.CrossRefGoogle Scholar
  34. 34.
    Chu, C. H., & Delp, E. J. (1989). Impulsive noise suppression and background normalization of electrocardiogram signals using morphological operators. IEEE Transactions on Biomedical Engineering, 36(2), 262–273.CrossRefGoogle Scholar
  35. 35.
    Hang, S. S., Lee, C., & Lee, M. (2009). Adaptive threshold method for the peak detection of photoplethysmographic waveform. Computers in Biology and Medicine, 39(12), 1145–1152.CrossRefGoogle Scholar
  36. 36.
    Maeda, Y., Sekine, M., & Tamura, T. (2011). The advantages of wearable green reflected photoplethysmography. Journal of Medical Systems, 35(5), 829–834.CrossRefGoogle Scholar

Copyright information

© Taiwanese Society of Biomedical Engineering 2017

Authors and Affiliations

  1. 1.Anhui Institute of Optics and Fine MechanicsChinese Academy of SciencesHefeiChina
  2. 2.University of Science and Technology of ChinaHefeiChina

Personalised recommendations