You are here

Improvement of the Heart Rate Estimation from the Human Facial Video Images

Journal Name:

Publication Year:

Abstract (2. Language): 
Human facial video captured with a webcam can be processed to extract the heart rate. This paper shows that the color space of processed video images, the distance of the person from a webcam and processed segment of the face are important parameters in the accuracy of the estimated heart rate. Our results show that the heart rate ought to be extracted from the facial videos in the HSV color space, also our results show that the accuracy of the estimated heart rate are decreased, by increasing the distance of the person from the webcam. Therefore, our proposed scheme using the video images captured by two webcams, improves the accuracy of the estimated heart rate. Subsequently, we segmented facial region to several blocks and found regions with higher accuracy in heart rate estimation. We have carried out experiments on a data set of 12 subjects. The results of experiment have been compared with the heart rates recorded by a fingertip pulse Oximeter in a statistical analysis framework.
16
23

REFERENCES

References: 

[1] S. Cook, M. Togni, M. C. Schaub, P. Wenaweser and O. M. Hess. “High heart rate: a cardiovascular risk factor?” European heart journal,vol. 27, pp. 2387-2393, 2006.
[2] M. Garbey, N. Sun, A. Merla, and I. Pavlidis, “Contact-free measurement of cardiac pulse based on the analysis of thermal imagery,” IEEE Trans. Biomed. Eng., vol. 54, pp. 1418–1426, Aug 2007.
[3] C. Takano and Y. Ohta, “Heart rate measurement based on a time-lapse image,” Medical engineering & physics, vol. 29, pp. 853–857, 2007.
[4] G. Balakrishnan, F. Durand, J. Guttag, “Detecting pulse from head motions in video,” IEEE Conference on Computer Vision and Pattern Recognition, pp. 3430 – 3437, June 2013.
[5] L.Wei, , Y. Tian, Y. Wang, T. Ebrahimi, and T. Huang, “Automatic webcam-based human heart rate measurements using laplasian eigenmap.” , pp. 281-292, 2013.
[6] M. Z. Poh, D. J. McDuffD, and R. W Picar, “Non-contact, automated cardiac pulse measurements using video imaging and blind source separation.” Optics Express, vol. 18, pp. 10762-10774, 2010.
[7] M. Z. Poh, D. J McDuffD, and R. W Picard, “Advanced in noncontact physiological measurements using a webcam.” IEEE transactions on biomedical engineering, vol. 58, pp. 7-11, 2011.
[8] D. T. Pham, and J. F. Cardoso, “Blind separation of instantaneous mixtures of non stationary sources.” IEEE Transactions on Signal Processing, vol. 49, pp. 1837-1848, 2001.
[9] P.Viola, and M. Jones. “Rapid object detection using a boosted cascade of simple features.” IEEE Conference on Computer Vision and Pattern Recognition, Los Alamitos, California, 8-14 December 2001: pp. 511-518, 2001.
[10] P.Viola, and M. Jones, “Robust real-time face detection”. International Journal of Computer Vision, vol. 57, pp. 137–154, 2004.
[11] M.P. Tarvainen, P.O Ranta-Aho and P.A Karjalainen, “An advanced detrending method with application to hrv analysis.” IEEE Transactions on Biomedical Engineering, vol. 49, pp. 172-175, 2002.
[12] J. F. Cardoso, and A. Souloumi-Ac, “Blind beam forming for non-Gaussian signals. in Radar and Signal Processing.” IEE Proceedings F (Radar and Signal Processing), vol. 140, pp. 362-370, 1993.
[13] J. F. Cardoso, “On the performance of orthogonal source separation algorithms.” Signal processing conference (EUSIPCO), Edinburgh, Scotland, Great Britain, pp. 776-779, September 1994.
Statistics
Experimental setup
mean
SD
RMSE
Webcam model
Color space
Distance from webcam
-3.2823
7.5547
8.6827
Hp
HSV
0.5m
-5.4994
23.6527
24.5251
Hp
RGB
6.0473
13.4615
14.3733
Hp
HSV
3m
0.5971
13.9336
13.9366
Dell
-1.1579
8.7868
9.0457
Hp,Del
International Journal of Science and Engineering Investigations, Volume 5, Issue 48, January 2016 23
www.IJSEI.com Paper ID: 54816-04
ISSN: 2251-8843
[14] J. F. Cardoso, “High-order contrasts for independent component analysis.” Neural computation, vol. 11, pp. 157-192, 1999.
[15] C. Andrzej, and S. I .Amari, Adaptive blind signal and image processing: learning algorithms and applications. New York: Wiley, 2002.
[16] From http://www.pulseoxstore.com/Manuals-Downloads.html
[17] G. A. F. Seber, Multivariate Observations: Hoboken, NJ: John Wiley & Sons, 1984.
[18] J. M. Bland, and D. G. Altman. “Statistical methods for assessing agreement between two methods of clinical measurement.” Lancet, vol. 84, pp. 307-310, 1986.

Thank you for copying data from http://www.arastirmax.com