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A highly efficient and reliable heart rate monitoring system using smartphone cameras

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Abstract

In the field of healthcare, a fundamental research topic includes the development of a vital signs monitoring system that allows people to monitor their health state so that they can control the same. Given that the heart is at the center of the human system, it is important to constantly monitor the heart beat rate. Hence, recently, heart rate monitoring systems or methods have started to use smartphones (and their built-in cameras) as computing and sensing platforms. These are ubiquitous devices. However, as most smartphones still have low computational power and as camera features differ across smartphones, smartphone-based heart rate computations can suffer from time-consuming processes. Hence, the results can be inaccurate or inconsistent. In this paper, we propose a highly efficient and reliable method to measure the heart rate from the smartphone camera images of fingertips. The method is composed of region of interest-based signal extraction, signal noise/bias reduction using an adaptive threshold scheme, and cycle miss/duplication handling using an iterative outlier elimination scheme. Existing methods require the real-time operation of high-speed processors and work properly on only specific smartphones. In contrast, the proposed method works consistently in real time with high accuracy on smartphones of any level. This is a very important factor because health-care facilities must be universally accessible, including to individuals who cannot afford buying excessively expensive high-performance smartphones.

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Notes

  1. Notice that the proposed signal extraction method can strengthen even the bias.

  2. In our experiments when the heart rates ranged from 60 to 110, each segment of 2.5 seconds length had three or four peaks or valleys. This was most suitable for estimating/offsetting the local bias variation with high precision and robustness.

  3. In the proposed method, signal enhancement similar to the smoothing differentiation has already been done in the previous step. Besides, it can amplify noise to apply only the signal differentiation as in [13]. Fixing the number of peaks may cause unstable heart rate results.

  4. Use of the frame-adaptive threshold [27] resulted in less reliable signals in the experiments with low frame resolutions.

  5. In preliminary experiments, the use of longer videos made little difference in accuracy. Specifically, when using 20 or 30 s long videos, the error rate in Eq. (5) was reduced by below 1 %. Furthermore, long measurement time (holding the finger over the camera lens for a long time) is not welcomed by users. Most existing systems used 5–15 s length [3, 5, 11, 13, 19].

  6. Some functionalities of the RenderScript [24] were not supported in low Anroid API levels and thus only the G3 was available.

References

  1. Baek H, Chung G, Kim K, Park K (2012) A smart health monitoring chair for nonintrusive measurement of biological signals. IEEE Trans Inf Technol Biomed 16(1):150–158

    Article  Google Scholar 

  2. Balakrishnan G, Durand F, Guttag J (2013) Detecting pulse from head motions in video. Proceedings of IEEE Conference on Computer Vision Pattern Recognition, Portland, pp 3430–3437

    Google Scholar 

  3. Banitsas K, Pelegris P, Orbach T, Cavouras D, Sidiropoulos K, Kostopoulos S (2009) A simple algorithm to monitor HR real time treatment applications. Proceedings of 9th International Conference on Information Technology and Applications in Biomedicine, Larnaca, pp 1–5

    Google Scholar 

  4. Blood pressure, https://en.wikipedia.org/wiki/Blood_pressure (accessed on 23 February, 2016)

  5. Chandrasekaran V (2010) Measuring Vital Signs Using Smart Phones. MS Thesis, University of North Texas, US

  6. Grimaldi D, Kurylyak Y, Lamonaca F, Nastro A (2011) Photo-plethysmography detection by smartphone’s video camera. Proceedings of the 6th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems, Prague, pp 488–491

    Google Scholar 

  7. Guede-Fernandez F, Ferrer-Mileo V, Ramos-Castro J, Fernandez-Chimeno M, Garcia-Gonzalez A (2015) Real time heart rate variability assessment from Android smartphone camera photoplethymography: postural and device influences. Proceedings of 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Milan, pp 7332–7335

    Google Scholar 

  8. Home health monitoring may significantly improve blood pressure control. Available online: http://xnet.kp.org/newscenter/pressreleases/co/2010/052110telehealthstudy.html (accessed on 23 September, 2015)

  9. Instant heart rate, https://play.google.com/store/apps/details?id=si.modula.android.instantheartrate (accessed on 19 January, 2016)

  10. Jonathan E, Leahy M (2010) Investigating a smartphone imaging unit for photoplethysmography. Physiol Meas 31:N79–N83

    Article  Google Scholar 

  11. Lagido RB, Lobo J, Leite S, Sousa C, Ferreira L, Silva-Cardoso J (2014) Using the smartphone camera to monitor heart rate and rhythm in heart failure patients. Proceedings of 2014 IEEE-EMBS International Conference on Biomedical and Health Informatics, Hotel Las Areanas Balneario Resort, pp 556–559

    Google Scholar 

  12. Lanata A, Scilingo EP, De Rossi D (2010) A multimodal transducer for cardiopulmonary activity monitoring in emergency. IEEE Trans Inf Technol Biomed 14(3):817–825

    Article  Google Scholar 

  13. Laure D, Paramonov I (2013) Improved algorithm for heart rate measurement using mobile phone camera. Proceedings of the 13th Conference of Open Innovations Association Fruct, Petrozavodsk, pp 85–93

    Google Scholar 

  14. Leonard P, Grubb N, Addison P, Clifton D, Watson J (2004) An algorithm for the detection of individual breaths from the pulse oximeter waveform. J Clin Monit Comput 18:309–312

    Article  Google Scholar 

  15. Maeda Y, Sekine M, Tamura T (2009) The advantages of wearable green reflected photoplethysmography. J Med Syst 35:829–834

    Article  Google Scholar 

  16. Michachelles F, Wicki R, Schiele B (2004) Less contact: heart-rate detection without even touching the user. Proceedings of Eighth International Symposium on Wearable Computers, Arlington, pp 4–7

    Google Scholar 

  17. Muaremi A, Armich B, Troster G (2013) Towards measuring stress with smartphones and wearable devices during workday and sleep. Bio Nano Sci 3(2):172–183

    Google Scholar 

  18. Nijboer J, Dorlas J, Mahieu H (1981) Photoelectric plethysmography: some fundamental aspects of the reflection and transmission method. Clin Phys Physiol Meas 2:205–215

    Article  Google Scholar 

  19. Pelegris P, Banitsas K, Orbach T, Marias K (2010) A novel method to detect heart beat rate using a mobile phone. Proceedings of 32nd Annual International Conference of the IEEE Engineering in Medicine Biology Society, Buenos Aires, pp 5488–5491

    Google Scholar 

  20. Po L-M, Xu X, Feng L, Li Y, Cheung K-W, Cheung C-H (2015) Frame adaptive ROI for photoplethymography signal extraction from fingertip video captured by smartphone. Proceedings of IEEE International Symposium on Circuits and Systems, Lisbon, pp 1634–1637

    Google Scholar 

  21. Poh M-Z, McDuff DJ, Picard RW (2010) Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Opt Express 18(10):10762–10774

    Article  Google Scholar 

  22. Poh M-Z, McDuff DJ, Picard RW (2011) A medical mirror for non-contact health monitoring. Proceedings of ACM SIGGRAPH 2011 Emerging Technologies, Vancouver, p 1

    Google Scholar 

  23. Reisner A, Shaltis P, McCombie D, Asada H (2008) Utility of the photoplethysmogram in circulatory monitoring. Anesthesiology 108:950–958

    Article  Google Scholar 

  24. RenderScript. https://developer.android.com/guide/topics/renderscript/compute.html (accessed on 14 August, 2016)

  25. Runtastic heart rate, https://play.google.com/store/apps/details?id=com.runtastic.android.heartrate.lite (accessed on 19 January, 2016)

  26. Scully CG, Lee JS, Meyer J, Gorbach AM, GranquistFraser D, Mendelson Y, Chon KH (2012) Physiological parameter monitoring from optical recordings with a mobile phone. IEEE Trans Biomed Eng 59:303–306

    Article  Google Scholar 

  27. Siddiqui SA, Zhang Y, Feng Z, Kos A (2016) A pulse rate estimation algorithm using PPG and smartphone camera. J Med Syst 40(5):126

    Article  Google Scholar 

  28. Verkruysse W, Svaasand LO, Nelson JS (2008) Remote plethysmo-graphic imaging using ambient light. Opt Express 16:21434–21445

    Article  Google Scholar 

  29. Walter M, Eilebrecht B, Wartzek T, Leonhardt S (2011) The smart car seat: personalized monitoring of vital signs in automotive applications. Pers Ubiquit Comput 15(7):707–715

    Article  Google Scholar 

  30. Wu K-F, Chan C-H, Zhang Y-T (2006) Contactless and cuffless monitoring of blood pressure on a chair using e-textile materials. Proceedings of 3rd IEEE/EMBS International Summer School on Medical Devices and Biosensors, Cambridge, pp 98–100

    Google Scholar 

  31. Zheng Y-L, Ding X-R, Poon CCY, Lo BPL, Zhang H, Zhou X-L, Yang G-Z, Zhao N, Zhang Y-T (2014) Unobtrusive sensing and wearable devices for health informatics. IEEE Trans Biomed Eng 61(5):1538–1554

    Article  Google Scholar 

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Acknowledgments

This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2014R1A1A2059579).

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Authors and Affiliations

  1. Department of Electronic Engineering, Pukyong National University, Busan, 48513, Korea

    Jean-Pierre Lomaliza & Hanhoon Park

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  1. Jean-Pierre Lomaliza
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Correspondence to Hanhoon Park.

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Lomaliza, JP., Park, H. A highly efficient and reliable heart rate monitoring system using smartphone cameras. Multimed Tools Appl 76, 21051–21071 (2017). https://doi.org/10.1007/s11042-016-4010-1

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  • DOI: https://doi.org/10.1007/s11042-016-4010-1

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