Comparative Analysis of RGB and Infrared Imaging Photoplethysmography Using Traditional and Deep Learning Approaches Across Various Frame Rates

Background: Imaging photoplethysmography (iPPG) offers a non-contact alternative for monitoring physiological signals, but optimal extraction of contact photoplethysmography (cPPG) signals across various camera frame rates and sensor modalities remains challenging.This study aims to systematically evaluate the performance of iPPG extraction using Red-Green-Blue (RGB) and infrared (IR) cameras at different frame rates (30 frames per second [fps] and 300 fps) under resting and exercise conditions, employing both traditional and deep learning-based approaches.Methods: Facial videos and corresponding cPPG signals were recorded simultaneously from 60 participants using RGB and IR cameras under 2 frame rate conditions (30 fps and 300 fps).Traditional iPPG extraction was conducted by averaging green channel intensity from selected regions of interest (ROIs), followed by bandpass filtering (0.5-5 Hz) and normalization.A deep learning approach using a convolutional neural network (CNN)based approach was also applied to reconstruct iPPG signals, processing entire facial regions without requiring specific ROIs.Reconstructed iPPG signals were compared with downsampled cPPG signals to match effective frame rates for evaluation. Results:The deep learning approach demonstrated superior performance for RGB cameras, achieving an average Pearson correlation of 0.996 0.002 for RGB and 0.994 0.009 for IR in the time domain.In the frequency domain, power spectral density (PSD) correlation reached 1.000 0.000 for RGB and 0.998 0.013 for IR.Traditional methods showed that 300 fps consistently provided higher PSD correlations (0.99 for RGB and 0.93 for IR) compared to 30 fps.Additionally, RGB cameras achieved approximately 10% higher Pearson correlations and 20% lower root mean square error (RMSE) compared to IR cameras across all conditions.The deep learning model demonstrated a remarkable improvement, reducing mean absolute error (MAE) and RMSE by approximately 50% compared to the traditional approach. Conclusion:The CNN-based method demonstrates superior performance over the traditional approach in reconstructing accurate iPPG signals.Additionally, higher frame rates (300 fps) provide substantial performance improvements, particularly for RGB cameras, which exhibited better signal fidelity compared to IR cameras.These findings suggest that deep