PULSE: Physiological Understanding with Liquid Signal Extraction

The non-contact estimation of vital signs, particularly heart rate, from video data is a promising method for remote health monitoring. 3D convolutional layers are widely used for this task due to their ability to capture both spatial and temporal features. However, traditional 3D convolutions, while effective in many cases, lack the capacity to adjust dy-namically to the temporal variability inherent in physiological signals such as remote photoplethysmography (rPPG), which are characterized by subtle frequency changes over time. To address this, we propose PULSE (Physiological Understanding with Liquid Signal Extraction), a frame-work that employs Liquid Time-Constant (LTC) models with 3D convolutional layers to enhance temporal sensitivity and improve the extraction of these fine-grained rPPG signals. In PULSE, traditional 3D-conv layers are deployed for ini-tial feature extraction, while LTC-based 3D-conv layers dy-namically adapt and guide the temporal processing, allowing the model to better track and interpret the subtle variations in heart rate signals under different conditions, such as motion artifacts and lighting changes. We evaluated the effectiveness of PULSE in an unsupervised training setting, demonstrating that our solution performs well even in the absence of labeled datasets a common challenge in rPPG signal extraction. Experimental evaluations on three public datasets confirm that PULSE achieves comparable or supe-rior results to existing methods, proving its robustness and efficacy for real-world, non-contact health monitoring applications.