Heart Rate Estimation from Remote Photoplethysmography Signal

Author(s)	Ms. Arya Sharma, Mr. Krishi Verma, Prof. Dr. Bhawna Sharma, Prof. Sheetal Gandotra, Ms. Ankita Sharma
Country	India
Abstract	Remote photoplethysmography (rPPG) enables noncontact heart rate estimation from facial videos by analyzing subtle color changes correlated with blood volume pulses. However, rPPG signals are often contaminated with motion artifacts, illumination noise, and facial expression changes. In this work, we present a GAN-based approach (PulseGAN) to denoise rPPG signals and improve heart rate estimation accuracy. Our pipeline extracts rPPG signals from facial regions in videos, employs PulseGAN to learn a mapping from noisy to clean signals, and estimates heart rate using frequency-domain analysis. We evaluate the performance on synchronized video and physiological datasets and demonstrate significant improvement in heart rate estimation accuracy over baseline rPPG processing. This study showcases the potential of deep generative models for robust physiological signal enhancement from video.
Field	Engineering
Published In	Volume 7, Issue 4, July-August 2025
Published On	2025-07-05
DOI	https://doi.org/10.36948/ijfmr.2025.v07i04.48505
Short DOI	https://doi.org/g9s88z

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International Journal For Multidisciplinary Research