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Smartphone-based heart-rate measurement using facial images and a spatiotemporal alpha-trimmed mean filter


Currently, cardiovascular disease affects a relatively high proportion of the world's population. Thus, developing simple and effective methods for monitoring patients with cardiovascular disease is critical for research. Monitoring the heart rate of patients is a relatively simple and effective method for managing patients with this condition. For patients, the desired heart rate monitoring equipment should be portable, instantaneous, and accurate. Because smartphones have become the most prevalent mobile device, we utilized this technology as a platform for developing a novel heart-rate measurement system. Catering to the phenomenon of people using the front camera of their smartphones as a mirror, the proposed system was designed to analyze facial-image sequences captured using the front camera. A spatiotemporal alpha-trimmed mean filter was developed to estimate a user's heart rate quickly and accurately. The experimental results show that in addition to achieving these objectives, the developed system outperforms a similar personal computer-based system. In addition, the system performs effectively even when users are wearing glasses. Hence, the proposed system demonstrates practical value for people who must monitor their heart rate daily.



Kyriacou E.C., , Pattichis C.S., and Pattichis M.S., An overview of recent health care support systems for eEmergency and mHealth applications. Proceedings of 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (2009), pp. 1246-1249.


Boulos M., , Wheeler S., , Tavares C., and Jones R., How smartphones are changing the face of mobile and participatory healthcare: An overview, with example from eCAALYX. BioMedical Engineering OnLine 10, 24 (2011).


Chen W.L., , Tsai T.H., , Huang C.C., , Chen J.H., and Kuo C.D., Heart rate variability predicts short-term outcome for successfully resuscitated patients with out-of-hospital cardiac arrest. Resuscitation 10, 1114 (2009).


Chiang J.K., , Koo M., , Kuo T.B., and Fu C.H., Association between cardiovascular autonomic functions and time to death in patients with terminal hepatocellular carcinoma. Journal of Pain and Symptom Management 4, 673 (2010).


Pickering T.G., White coat hypertension. Current Opinion in Nephrology and Hypertension 2, 192 (1996).


Nicolaides A.N., Diagnostic evaluation of patients with chronic venous insufficiency, Vascular Surgery, edited by R.B. Rutherford, WB Saunders Company (1989).


Hull R., , van Aken W.G., , Hirsh J., , Gallus A.S., , Hoicka G., , Turpie A.G.G., , Walker I., and Gent M., Impedance plethysmography using the occlusive cuff technique in the diagnosis of venous thrombosis. Circulation 1, 696 (1976).


Hertzman A.B., Photoelectric plethysmograph of the fingers and toes in man. Proceedings of the Society for Experimental Biology and Medicine (1937), p. 529.


Whitney R.J., The measurement of volume changes in human limbs. Journal of Applied Physiology 1, 1 (1953).


Poh M.Z., , McDuffk D.J., and Picard R.W., Non-contact, automated cardiac pulse measurements using video imaging and blind source separation. Optics Express 10, 10762 (2010).


Chen D.Y., , Wang J.J., , Lin K.Y., , Chang H.H., , Wu H.K., , Chen Y.S., and Lee S.Y., Image sensor-based heart rate evaluation from face reflectance using Hilbert-Huang transform. IEEE Sensors Journal 1, 618 (2015).