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Author(s): Kokila Bharti Jaiswal

Email(s): kbjaiswal.phd2018.etc@nitrr.ac.in

Address: Department of Electronics and Communication, National Institute of Technology, Raipur.

*Corresponding Author: kbjaiswal.phd2018.etc@nitrr.ac.in.

Published In:   Volume - 36,      Issue - 1,     Year - 2023

DOI: 10.52228/JRUB.2023-36-1-2  

Mortality rate in Chhattisgarh state due to ischemic heart disease is 43.6% and growing exponentially every year. Early detection of cardiac health plays a major role in decreasing this rate. Due to the insufficient hospitals and accessibility of the dedicated equipment, remote health monitoring has become quite inevitable after SARC-CoV-2 pandemic. Due to its excellent capability is it going to be cardiac rate measurement method of future. However, the difficulty in HR measurement is that, it gets affected with noise very easily because the amplitude of physiological signal is very weak. remote Photoplethysmography (rPPG) is a technique to measure the cardiac activity in a contact-less manner using digital cameras. However, the HR estimation suffers from two major artifacts, motion artifact and illumination artifact. Denoising of rPPG signal is a fundamental problem and needs to be addressed very carefully. In this article we have proposed a novel HR estimation network using a combination of wavelet decomposition and Convolutional Neural Network (CNN). This approach provides distinct features at different frequency levels, which facilitates the removal of noisy signal. Performance evaluation of the proposed method is done on self-collected dataset. Lower values of RMSE and MAE proves the efficacy of the proposed method.

Cite this article:
Kokila Bharti Jaiswal (2023). Development of Non-Invasive Technique for Heart Rate Detection Using Facial Videos. Journal of Ravishankar University (Part-B: Science), 36(1), pp. 12- 17.DOI: https://doi.org/10.52228/JRUB.2023-36-1-2


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[8] Qiu, Y., Liu, Y., Arteaga-Falconi, J., Dong, H., & El Saddik, A. (2018). EVM-CNN: Real-time contactless heart rate estimation from facial video. IEEE transactions on multimedia21(7), 1778-1787.

[9] S. Bobbia, R. Macwan, Y. Benezeth, A. Mansouri, J. Dubois, "Unsupervised skin tissue segmentation for remote photoplethysmography", Pattern Recognition Letters, 2017.

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