MaKAN-Mixer: Channel Interaction-Based Mamba Method for rPPG Extraction.

Abstract

Remote photoplethysmography (rPPG) achieves non-contact heart rate monitoring by detecting subtle skin color variations in facial videos, offering significant potential in healthcare, fitness, and security applications.However, accurately extracting rPPG signals in complex environments-especially under variable lighting and motion artifacts-remains challenging. The main difficulties are capturing spatio-temporal dynamics and modeling long-term dependencies across channels. To address these limitations, we propose MaKAN-Mixer, a novel end-to-end network designed to enhance the robustness and accuracy of rPPG signal extraction. First, MaKAN-Mixer integrates a Hybrid of Eulerian Video Magnification and Temporal Shift Module Amplification (HETA) to amplify subtle physiological signals and enhance temporal information without relying on explicit region-of-interest (ROI) selection. Additionally, we propose the Mamba-KAN Fusion Module (MKFM), which leverages Mamba's ability to efficiently model long-term dependencies in temporal sequences. By incorporating the Kolmogorov-Arnold Network (KAN) for effective channel mixing, MKFM ensures the comprehensive fusion of relevant spatio-temporal features across different channels. Finally, we employ a KAN Feedforward Neural Network (KFN) to capture complex, nonlinear, and periodic physiological patterns, improving heart rate estimation. Extensive experiments conducted on four benchmark datasets demonstrate that MaKAN-Mixer achieves superior performance in both intra- and cross-dataset testing, exhibiting exceptional robustness in challenging scenarios, particularly with compressed video data and complex environments. In comparison to the best-performing existing method, which reported RMSE values of 0.78/0.47/4.57/6.81 on the four datasets, MaKAN-Mixer significantly improves the RMSE to 0.66/0.40/0.32/6.25, highlighting its effectiveness across diverse conditions. Furthermore, novel visualization techniques were employed for qualitative validation of the results, underscoring its potential for accurate, real-world rPPG monitoring.