Real-Time Heart Rate Estimation Using a Standard Camera

Khidhir, Ali

doi:10.25673/122074

Proceedings of International Conference on Applied Innovation in IT · 2025/08/29 · Vol. 13 · Issue 4 · pp. 109–116

Real-Time Heart Rate Estimation Using a Standard Camera

Ali Sameer Salim and Abdul Sattar Mohammed Khidhir

📄 Download PDF DOI: 10.25673/122074

Abstract

Health indicators are among the most important ways to assess health status and predict sudden health relapses. Non-contact health monitoring is critical for overcoming limitations of traditional vital sign measurement methods, such as infection risks and patient discomfort during continuous monitoring. This study will present a method for real-time heart rate measuring without contact with people by using a standard webcam and applying processing on the extracted signal from frames of video, applying a Butterworth filter, Chrominance-based method algorithm to obtain a signal similar to the heart rate signal, then calculate the heart rate using two methods, the first peaks detection and second method by Fast Fourier transform (FFT) and compare between them. These Experiments were conducted indoors with a lighting source in front of participants, and the number of participants was 15. The results obtained from the proposed method were compared with those of the Pulse Oximeter, which was medically approved, and the final results of median absolute error (MAE) were 1.28 for the Peak detection method and 2.47 for the FFT method. These results demonstrate the potential of webcam-based heart rate monitoring as a reliable, non-invasive alternative for health assessment.

Keywords

Remote Photoplethysmography (rPPG) Non-Contact Heart Rate Monitoring Remote Health Monitoring Computer Vision Deep Learning.

References

A. Al-Naji, K. Gibson, S. H. Lee, and J. Chahl, “Monitoring of cardiorespiratory signal: Principles of remote measurements and review of methods,” IEEE Access, vol. 5, pp. 15776–15790, Aug. 2017, doi: 10.1109/ACCESS.2017.2735419.
X. Chen, J. Cheng, R. Song, Y. Liu, R. Ward, and Z. J. Wang, “Video-based heart rate measurement: Recent advances and future prospects,” IEEE Trans. Instrum. Meas., vol. 68, no. 10, pp. 3600–3615, Oct. 2019, doi: 10.1109/TIM.2018.2879706.
K. M. van der Kooij and M. Naber, “An open-source remote heart rate imaging method with practical apparatus and algorithms,” Behav. Res. Methods, vol. 51, no. 5, pp. 2106–2119, Oct. 2019, doi: 10.3758/s13428-019-01256-8.
M.-Z. Poh, D. J. McDuff, and R. W. Picard, “Non-contact, automated cardiac pulse measurements using video imaging and blind source separation,” in Proc. Conf., 2010.
M. Lewandowska and J. Nowak, “Measuring pulse rate with a webcam,” J. Med. Imaging Health Inform., vol. 2, no. 1, pp. 87–92, Mar. 2012, doi: 10.1166/jmihi.2012.1064.
L. Wei, Y. Tian, Y. Wang, T. Ebrahimi, and T. Huang, “Automatic webcam-based human heart rate measurements using Laplacian eigenmap,” in Lecture Notes in Computer Science, vol. 7725, 2012.
H. Y. Wu, M. Rubinstein, E. Shih, J. Guttag, F. Durand, and W. Freeman, “Eulerian video magnification for revealing subtle changes in the world,” ACM Trans. Graph., vol. 31, no. 4, Jul. 2012, doi: 10.1145/2185520.2185561.
G. De Haan and V. Jeanne, “Robust pulse rate from chrominance-based rPPG,” IEEE Trans. Biomed. Eng., vol. 60, no. 10, pp. 2878–2886, 2013, doi: 10.1109/TBME.2013.2266196.
W. Wang, A. C. Den Brinker, S. Stuijk, and G. De Haan, “Robust heart rate from fitness videos,” Physiol. Meas., vol. 38, no. 6, pp. 1023–1044, May 2017, doi: 10.1088/1361-6579/aa6d02.
B. Huang, C. L. Lin, W. Chen, C. F. Juang, and X. Wu, “A novel one-stage framework for visual pulse rate estimation using deep neural networks,” Biomed. Signal Process. Control, vol. 66, Apr. 2021, doi: 10.1016/j.bspc.2020.102387.
R. A. Firmansyah, Y. A. Prabowo, T. Suheta, and S. Muharom, “Implementation of 1D convolutional neural network for improvement remote photoplethysmography measurement,” Indonesian J. Elect. Eng. Comput. Sci., vol. 29, no. 3, pp. 1326–1335, Mar. 2023, doi: 10.11591/ijeecs.v29.i3.pp1326-1335.
P. V. Rouast, M. T. P. Adam, R. Chiong, D. Cornforth, and E. Lux, “Remote heart rate measurement using low-cost RGB face video: A technical literature review,” Front. Comput. Sci., vol. 12, no. 5, pp. 858–872, Oct. 2018, doi: 10.1007/s11704-016-6243-6.
C. H. Cheng, K. L. Wong, J. W. Chin, T. T. Chan, and R. H. Y. So, “Deep learning methods for remote heart rate measurement: A review and future research agenda,” Sensors, vol. 21, no. 18, Sep. 2021, doi: 10.3390/s21186296.
R. J. Lee, S. Sivakumar, and K. H. Lim, “Review on remote heart rate measurements using photoplethysmography,” Multimed. Tools Appl., vol. 83, no. 15, pp. 44699–44728, May 2024, doi: 10.1007/s11042-023-16794-9.
W. Chen, H. Huang, S. Peng, C. Zhou, and C. Zhang, “YOLO-face: A real-time face detector,” Vis. Comput., vol. 37, no. 4, pp. 805–813, Apr. 2021, doi: 10.1007/s00371-020-01831-7.
O. A. Naser, S. M. S. Ahmad, K. Samsudin, M. Hanafi, S. M. B. Shafie, and N. Z. Zamri, “Facial recognition for partially occluded faces,” Indonesian J. Elect. Eng. Comput. Sci., vol. 30, no. 3, pp. 1846–1855, Jun. 2023, doi: 10.11591/ijeecs.v30.i3.pp1846-1855.
W. Wu, H. Peng, and S. Yu, “YuNet: A tiny millisecond-level face detector,” Mach. Intell. Res., vol. 20, no. 5, pp. 656–665, Oct. 2023, doi: 10.1007/s11633-023-1423-y.
G. Amato, F. Falchi, C. Gennaro, and C. Vairo, “A comparison of face verification with facial landmarks and deep features,” in Proc. Int. Conf. Advances Multimedia (MMEDIA), Pisa, Italy, 2018.
V. Kazemi and J. Sullivan, “One millisecond face alignment with an ensemble of regression trees,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit. (CVPR), pp. 1867–1874, 2014.
K. Ramyasree and C. S. Kumar, “Acoustic and visual geometry descriptor for multi-modal emotion recognition from videos,” Indonesian J. Elect. Eng. Comput. Sci., vol. 33, no. 2, pp. 960–970, Feb. 2024, doi: 10.11591/ijeecs.v33.i2.pp960-970.
I. Berezhnyi and A. Nakonechnyi, “Analysis of methods and algorithms for remote photoplethysmography signal diagnostic and filtering,” Adv. Cyber-Phys. Syst., vol. 9, no. 1, pp. 82–88, May 2024, doi: 10.23939/acps2024.01.082.
S. F. Hussin, G. Birasamy, and Z. Hamid, “Design of Butterworth band-pass filter,” Politeknik & Kolej Komuniti J. Eng. Technol., vol. 1, pp. 32–46, 2016.
S. Akdemir Akar, S. Kara, F. Latifoglu, and V. Bilgiç, “Spectral analysis of photoplethysmographic signals: The importance of preprocessing,” Biomed. Signal Process. Control, vol. 8, no. 1, pp. 16–22, Jan. 2013, doi: 10.1016/j.bspc.2012.04.002.
A. Savitzky and M. J. E. Golay, “Smoothing and differentiation of data by simplified least squares procedures,” Anal. Chem., vol. 36, no. 8, pp. 1627–1639, Jul. 1964.
F. Pierre, J. F. Aujol, A. Bugeau, N. Papadakis, and V. T. Ta, “Luminance-chrominance model for image colorization,” SIAM J. Imaging Sci., vol. 8, no. 1, pp. 536–563, Mar. 2015, doi: 10.1137/140979368.
Q. Qin, J. Li, Y. Yue, and C. Liu, “An adaptive and time-efficient ECG R-peak detection algorithm,” J. Healthc. Eng., vol. 2017, 2017, doi: 10.1155/2017/5980541.