STSPhys: Enhanced Remote Heart Rate Measurement With Spatial-Temporal SwiftFormer

Abstract

Estimating heart activities and physiological signals from facial video without any contact, known as remote photoplethysmography and remote heart rate estimation, holds significant potential for numerous applications. In this letter, we present a novel approach for remote heart rate measurement leveraging a Spatial-Temporal SwiftFormer architecture (STSPhys). Our model addresses the limitations of existing methods that rely heavily on 3D CNNs or 3D visual transformers, which often suffer from increased parameters and potential instability during training. By integrating both spatial and temporal information from facial video data, STSPhys achieves robust and accurate heart rate estimation. Additionally, we introduce a hybrid loss function that integrates constraints from both the time and frequency domains, further enhancing the model's accuracy. Experimental results demonstrate that STSPhys significantly outperforms existing state-of-the-art methods on intra-dataset and cross-dataset tests, achieving superior performance with fewer parameters and lower computational complexity.