Volume 19, Issue 7 (2019)                   Modares Mechanical Engineering 2019, 19(7): 1751-1757 | Back to browse issues page

XML Persian Abstract Print


Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Sokout M, Heidary S, Beigzadeh B. Heart Rate Measurement with Imaging Photoplethysmography Signals Using Smart Phone. Modares Mechanical Engineering. 2019; 19 (7) :1751-1757
URL: http://journals.modares.ac.ir/article-15-24532-en.html
1- Mechanical Department, Mechanical Engineering Faculty, Iran University of Science & Technology, Tehran, Iran
2- Mechanical Department, Mechanical Engineering Faculty, Iran University of Science & Technology, Tehran, Iran , b_beigzadeh@iust.ac.ir
Abstract:   (131 Views)

Measuring the vital signs of human body, such as oxygen saturation, blood pressure, and heart rate is the greatest and basic stage of the diagnosis of various diseases, especially cardiovascular diseases. Various methods have been developed to measure these signs. In general, these methods are divided into invasive and non-invasive categories. Due to less damages of non-invasive methods, more attention has been paid to them in recent decades. Using mobile phone is one of the most important non-invasive approaches because of being common and accessible among people. In this article, after studying Photoplethysmographic methods and expressing theories related to this method, imaging photoplethysmography (IPPG) is used to measure heart rate. Regarding two proposed algorithms based on furies transfer and peak detection, implementation of these algorithms was done, using a camera and LED of smart phone on 20 people. Next, the heart was calculated. Finally, a comparison was made between the two methods, the results of which show that peak detection method has less error than furies transfer method.

Full-Text [PDF 546 kb]   (91 Downloads)    

Received: 2018/08/28 | Accepted: 2019/01/13 | Published: 2019/07/1

References
1. Zhang Z, Pi Z, Liu B. Troika: A general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise. IEEE Transactions on Biomedical Engineering. 2014;62(2):522-531. [Link] [DOI:10.1109/TBME.2014.2359372]
2. Selvaraj N, Jaryal A, Santhosh J, Deepak KK, Anand S. Assessment of heart rate variability derived from finger-tip photoplethysmography as compared to electrocardiography. Journal of Medical Engineering and Technology. 2008;32(6):479-484. [Link] [DOI:10.1080/03091900701781317]
3. Asada HH, Shaltis P, Reisner A, Rhee S, Hutchinson RC. Mobile monitoring with wearable photoplethysmographic biosensors. IEEE Engineering in Medicine and Biology Magazine. 2003;22(3):28-40. [Link] [DOI:10.1109/MEMB.2003.1213624]
4. Camm A, Malik M, Bigger J, Breithardt G, Cerutti S, Cohen R, et al. Heart rate variability: Standards of measurement, physiological interpretation and clinical use, task force of the European society of cardiology and the north American society of pacing and electrophysiology. Circulation. 1996;93(5):1043-1065. [Link] [DOI:10.1161/01.CIR.93.5.1043]
5. Bauer A, Malik M, Schmidt G, Barthel P, Bonnemeier H, Cygankiewicz I, et al. Heart rate turbulence: Standards of measurement, physiological interpretation, and clinical use: International Society for Holter and Noninvasive Electrophysiology Consensus. Journal of the American College of Cardiology. 2008;52(17):1353-1365. [Link] [DOI:10.1016/j.jacc.2008.07.041]
6. Weippert M, Kumar M, Kreuzfeld S, Arndt D, Rieger A, Stoll R. Comparison of three mobile devices for measuring R-R intervals and heart rate variability: Polar S810i, Suunto t6 and an ambulatory ECG system. European Journal of Applied Physiology. 2010;109(4):779-786. [Link] [DOI:10.1007/s00421-010-1415-9]
7. Anttonen J, Surakka V. Emotions and heart rate while sitting on a chair. CHI '05 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, April 02 - 07, 2005, Portland, Oregon, USA. New York: ACM; 2005. p. 491-499. [Link] [DOI:10.1145/1054972.1055040]
8. Li X, Chen J, Zhao G, Pietikainen M. Remote heart rate measurement from face videos under realistic situations. 2014 IEEE Conference on Computer Vision and Pattern Recognition, 23-28 June 2014, Columbus, OH, USA. Piscataway: IEEE; 2014. [Link] [DOI:10.1109/CVPR.2014.543]
9. Takano C, Ohta Y. Heart rate measurement based on a time-lapse image. Medical Engineering and Physics. 2007;29(8):853-857. [Link] [DOI:10.1016/j.medengphy.2006.09.006]
10. Poh MZ, McDuff DJ, Picard RW. Advancements in noncontact, multiparameter physiological measurements using a webcam. IEEE Transactions on Biomedical Engineering. 2011;58(1):7-11. [Link] [DOI:10.1109/TBME.2010.2086456]
11. Lamonaca F, Polimeni G, Barbé K, Grimaldi D. Health parameters monitoring by smartphone for quality of life improvement. Measurement. 2015;73:82-94. [Link] [DOI:10.1016/j.measurement.2015.04.017]
12. Al-Mardini M, Aloul F, Sagahyroon A, Al-Husseini L. Classifying obstructive sleep apnea using smartphones. Journal of Biomedical Informatics. 2014;52:251-259. [Link] [DOI:10.1016/j.jbi.2014.07.004]
13. Harrison C, Hudson SE. Scratch input: Creating large, inexpensive, unpowered and mobile finger input surfaces. UIST '08 Proceedings of the 21st Annual ACM Symposium on User Interface Software and Technology, 19 - 22 October 2008, Monterey, CA, USA. New York: ACM; 2008. p. 205-208. [Link] [DOI:10.1145/1449715.1449747]
14. Xu X, Akay A, Wei H, Wang S, Pingguan-Murphy B, Erlandsson BE, et al. Advances in smartphone-based point-of-care diagnostics. Proceedings of the IEEE. 2015;103(2):236-247. [Link] [DOI:10.1109/JPROC.2014.2378776]
15. Hertzman AB, Spealman CR. Observations on the finger volume pulse recorded photo-electrically. American Journal of Physiology. 1937;119:334-335. [Link]
16. Kamal AA, Harness JB, Irving G, Mearns AJ. Skin photoplethysmography-a review. Computer Methods and Programs in Biomedicine. 1989;28(4):257-269. [Link] [DOI:10.1016/0169-2607(89)90159-4]
17. Tamura T, Maeda Y, Sekine M, Yoshida M. Wearable photoplethysmographic sensors-past and present. Electronics. 2014;3(2):282-302. [Link] [DOI:10.3390/electronics3020282]
18. Lee CM, Zhang YT. Reduction of motion artifacts from photoplethysmographic recordings using a wavelet denoising approach. IEEE EMBS Asian-Pacific Conference on Biomedical Engineering, 20-22 Oct. 2003, Kyoto, Japan, Japan. Piscataway: IEEE; 2004. [Link]
19. Joseph G, Joseph A, Titus G, Thomas RM, Jose D. Photoplethysmogram (PPG) signal analysis and wavelet de-noising. 2014 Annual International Conference on Emerging Research Areas: Magnetics, Machines and Drives (AICERA/iCMMD), 24-26 July 2014, Kottayam, India. Piscataway: IEEE; 2014. [Link] [DOI:10.1109/AICERA.2014.6908199]
20. Laure D, Paramonov I. Improved algorithm for heart rate measurement using mobile phone camera. 13th Conference of Open Innovations Association (FRUCT), 22-26 April 2013, Petrozavodsk, Russia. Piscataway: IEEE; 2017. [Link] [DOI:10.23919/FRUCT.2013.8124232]
21. Leonard P, Grubb NR, Addison PS, Clifton D, Watson JN. An algorithm for the detection of individual breaths from the pulse oximeter waveform. Journal of Clinical Monitoring and Computing. 2004;18(5-6):309-312. [Link] [DOI:10.1007/s10877-005-2697-z]
22. Pelegris P, Banitsas K, Orbach T, Marias K. A novel method to detect heart beat rate using a mobile phone. Annual International Conference of the IEEE Engineering in Medicine and Biology, 31 Aug-4 Sept 2010, Buenos Aires, Argentina. Piscataway: IEEE; 2010. [Link] [DOI:10.1109/IEMBS.2010.5626580]
23. Lomaliza JP, Park H. Improved peak detection technique for robust PPG-based heartrate monitoring system on smartphones. Multimedia Tools and Applications. 2018;77(13):17131-17155. [Link] [DOI:10.1007/s11042-017-5282-9]
24. Oak SS, Aroul P. How to Design Peripheral Oxygen Saturation (SpO2) and Optical Heart Rate Monitoring (OHRM) Systems Using the AFE4403. Texas Instruments. 2015. [Link]
25. Maeda Y, Sekine M, Tamura T. The advantages of wearable green reflected photoplethysmography. Journal of Medical Systems. 2011;35(5):829-834. [Link] [DOI:10.1007/s10916-010-9506-z]
26. Wang D, Zhang D, Lu G. A robust signal preprocessing framework for wrist pulse analysis. Biomedical Signal Processing and Control. 2016;23:62-75. [Link] [DOI:10.1016/j.bspc.2015.08.002]

Add your comments about this article : Your username or Email:
CAPTCHA

Send email to the article author