The ubiquitous use and advancement in built-in smartphone sensors and the development in big data processing have been beneficial in several fields including healthcare. Among the basic vitals monitoring, pulse rate monitoring is the most important healthcare necessity. A multimedia video stream data acquired by built-in smartphone camera can be used to estimate it. In this paper, an algorithm that uses only smartphone camera as a sensor to estimate pulse rate using PhotoPlethysmograph (PPG) signals is proposed. The results obtained by the proposed algorithm are compared with the actual pulse rate and the maximum error found is 3 beats per minute. The standard deviation in percentage error and percentage accuracy is found to be 0.68 % whereas the average percentage error and percentage accuracy is found to be 1.98 % and 98.02 % respectively.
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He, X., Goubran, R. A., Fellow, and Liu, X. P., Secondary peak detection of PPG signal for continuous cuffless arterial blood pressure measurement. IEEE Trans. Instrum. Meas. 63(6):1431–1439, 2014.
Tsu-Hsun, F., Liu, S.-H., and Tang, K.-T., Heart rate extraction from photoplethysmogram waveform using wavelet multi-resolution analysis. J. Med. Biol. Eng. 28(4):229–232, 2008.
Li, H and Zhao H., Systolic blood pressure estimation using Android smart phones, 6th International Conference on Biomedical Engineering and Informatics (BMEI). 260–264, 2013.
Suzuki, A., and Ryu, K., Feature selection method for estimating systolic blood pressure using the taguchi method. IEEE Trans. Indust. Inform. 10(2):1077–1085, 2014.
Shukla, S. N., Kakwani, K., Patra, A., Lahkar, B. K., Gupta, V. K., Jayakrishna, A., Vashisht, P., and Sreekanth, I., Noninvasive Cuffless blood pressure measurement by vascular transit time. 28th Int. Conf. VLSI Des. 14th Int. Conf. Embedded Syst. 535–540, 2015.
Chen, Z., Yang, X., Teo, J. T., and Ng, S. H., Noninvasive monitoring of blood pressure using optical ballistocardiography and photoplethysmograph approaches*. IEEE 35th Ann. Int. Conf. EMBS Osaka, Japan. 2425–2428 2013.
Visvanathan, A., Sinha, A. and Pal, A., Estimation of blood pressure levels from reflective photoplethysmograph using smart phones. IEEE 13 th Int. Conf. Bioinform. Bioeng. (BIBE). 2013.
Chou, C. -C., Huang, W. -C. and Fang, W. -C., An effective three way PPG acquiring and signal processing system by using square wave modulation. IEEE Int. Conf. Consumer Electron. (ICCE). 366–369, 2015.
Kondo, R., Bhuiyan, Md. S., Kawanaka, H. and Oguri, K., Separate estimation of long- and short-term systolic blood pressure variability from photoplethysmograph. IEEE 36 th Ann. Int. Conf. Eng. Med. Biol. Soc. (EMBC). 1851–1854, 2014.
Kumar, N., Agrawal, A., Deb, S., Cuffless BP measurement using a correlation study of pulse transient time and heart rate. Int. Conf. Adv. Comput., Commun. Inform. (ICACCI). 1538–1541, 2014.
Lourdes Albina Nirupa, J. and Jagadeesh Kumar V., Non-invasive measurement of hemoglobin content in blood. IEEE Int. Symp. Med. Measure. Appl. (MeMeA). 1–5, 2014.
Harbawi, M. A., Ibrahimy, M. I. and Motakabber, S. M. A., Photoplethysmography based remote health monitoring system. Proc. IEEE Int. Conf. Smart Instrument., Measure. Appl. (ICSIMA), Kuala Lumpur, Malaysia. 2013.
He, X., Goubranand, R. A. and Liu, X. P., Evaluation of the correlation between blood pressure and pulse transit time. IEEE Int. Symp. Med. Measure. Appl. Proc. (MeMeA). 2013.
Kwon, S., Kim, H. and Park, K. S., Validation of heart rate extraction using video imaging on a built-in camera system of a smartphone. Ann. Int. Conf. Eng. Med. Biol. Soc. (EMBC’12). 2174–2177, 2012.
Peng, R. C., Zhou, X. -C, Lin, W -H. and Zhang, Y. -T., Extraction of heart rate variability from smartphone photoplethysmograms. Comput. Math. Methods Med. 1–11, 2015.
Girish Rao Salanke, N. S., Samraj, A., Maheswari, N. and Sadhasivam, S., Enhancement in the design of biometric identification system based on photoplethysmography data. Proc. Int. Conf. Green High Perform. Comput. (ICGHPC), India. 1–6, 2013.
Zhang, Z., Pi, Z., and Liu, B., TROIKA: a general framework for heart rate monitoring using wrist-type photoplethysmographic signals during intensive physical exercise. IEEE Trans. Biomed. Eng. 62(2):522–531, 2015.
Lee, B.-G., and Chung, W.-Y., Driver alertness monitoring using fusion of facial features and bio-signals. IEEE Sensors J. 12(7):2416–2422, 2012.
Elgendi, M., On the analysis of fingertip photoplethysmogram signals. Curr. Cardiol. Rev. 8:14–25, 2012.
Naraharisetti, K. V. P. and Bawa, M., Comparison of different signal processing methods for reducing artifacts from photoplethysmograph signal. IEEE Int. Conf. Electro/Inform. Technol. (EIT). 1–8, 2011.
Utami, N., Setiawan, A. W., Zakaria, H., Mengko, T. R. and Mengko, R., Extracting blood flow parameters from photoplethysmograph signals: a review. 3rd Int. Conf. Instrument., Commun., Inform. Technol. Biomed. Eng. (ICICI-BME), Bandung. 403–407, 2013.
Shafqat, K., Langford, M., Pal, S. K. and Kyriacou, P. A., Estimation of venous oxygenation saturation using the finger Photoplethysmograph (PPG) waveform. IEEE 34th Ann. Int. Conf. EMBS, San Diego, California USA. 2905–2908, 2012.
Lee, C., SikShin, H. and Lee, M., Relations between ac-dc components and optical path length in photoplethysmography. J. Biomed. Optics. 16(7). 2011.
Lee, Y.-K., Kwon, O.-W., Shin, H. Y., Jo, J. and Lee, Y., Noise reduction of PPG signals using a particle filter for robust emotion recognition. IEEE Int. Conf. Consumer Electron-Berlin (ICCE-Berlin). 202–205, 2011.
Lao, C. K., Kin Che, U., Chen, W., Pun, S. H., Un Mak, P., Wan, F. and Vai, M. I., Portable heart rate detector based on photoplethysmography with android programmable devices for ubiquitous health monitoring system. Int. J. Adv. Telecomm. Electrotech., Sign. Syst. 2013.
Li, D., Zhao, H., and Dou, S., Anew signal decomposition to estimate breathing rate and heart rate from Photoplethysmography signal. Biomed. Sign. Process. Contrl. 19:89–95, 2015.
Lavanya, M. P., Real time motion detection using background subtraction method and frame difference. Int. J. Sci. Res. (IJSR) 3(6):1857–1861, 2014.
Singla, N., Motion detection based on frame difference method. Int. J. Inform.Comput.Technol. 4(15):1559–1565, 2014.
This work was supported in part by the National Natural Science Foundation of China under Grant 61572231, 61173079 and 61472163, in part by the Natural Science Foundation of Shandong, China under Grant ZR2013FM004. The corresponding author is Yuan Zhang.
This article is part of the Topical Collection on Mobile Systems
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Siddiqui, S.A., Zhang, Y., Feng, Z. et al. A Pulse Rate Estimation Algorithm Using PPG and Smartphone Camera. J Med Syst 40, 126 (2016). https://doi.org/10.1007/s10916-016-0485-6
- Pulse rate
- PhotoPlethysmoGraph (PPG)
- Smartphone sensor
- Mobile health