PULSE: Physiological Understanding with Liquid Signal Extraction

Shahzad Ahmad, Sania Bano, Sachin Verma, Yogesh Singh Rawat, Sukalpa Chanda, Santosh Kumar Vipparthi, Subrahmanyam Murala; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 4574-4584

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 dynamically 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 framework 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 initial feature extraction while LTC-based 3D-conv layers dynamically 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 superior results to existing methods proving its robustness and efficacy for real-world non-contact health monitoring applications.