Do deep learning methods improve the accuracy of non-contact heart rate estimation using remote photoplethysmography compared to conventional signal processing techniques?
Deep learning algorithms enhance the accuracy of remote photoplethysmography for non-contact heart rate monitoring compared to conventional signal processing methods.
With the widespread availability of consumer-grade cameras, interest in heart rate (HR) measurement using remote photoplethysmography (rPPG) has grown significantly. rPPG is a noninvasive optical technique that uses camera to measure heart rate by analyzing light reflectance due to blood flow changes beneath the skin from any parts of the body, mostly facial regions. However, it faces challenges such as motion artifacts and sensitivity to varying lighting conditions. The rapid advancement of deep learning techniques in recent years has driven numerous studies to integrate these models with rPPG for HR detection in remote health monitoring systems. This study provides a comprehensive review of both conventional approaches and recent developments in rPPG and deep learning algorithms. A comparative analysis highlighted the superior accuracy of deep learning methods over conventional techniques in non-contact HR estimation. Based on a review of 145 articles encompassing different methodologies, signal processing strategies, and deep learning algorithms, our study identifies existing research gaps and explores future research opportunities for real-world applications.
Debnath et al. (Fri,) studied this question.
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