REVIT: A Dataset and Benchmark for Remote Vital Sign Estimation in Real-World Settings

Abstract

Video-based physiological signal estimation is crucial for non-invasive and contactless patient monitoring in scenarios where traditional sensor-based methods are impractical, such as telemedicine and large-scale population health studies. The development and efficacy of such approaches is critically influenced by the availability of representative human subject datasets that reflect real-world scenarios. However, existing datasets predominantly consist of recordings collected in controlled laboratory settings with limited diversity in environmental conditions and demographic representation. Such constraints hinder the generalizability and robustness of video-based physiological signal estimation approaches in real-world scenarios (due to real-world scenarios demonstrating a challenging distribution of environmental diversity as compared to laboratory conditions). To this end, we present the REmote VITals (REVIT) dataset, comprising of 1553 human subject videos and associated ground truth physiological sign measurements (such as heart rate) collected in multiple outside-the-lab settings for the remote estimation of human physiological signs. The dataset is uniquely designed to capture a wide range of environmental diversity (such as variable lighting and background) encountered in outside-the-lab settings, thereby enabling the development of more robust physiological signal estimation approaches. Through statistical hypothesis testing, we demonstrate that utilizing the REVIT dataset results in a significant improvement in the robustness of existing video-based physiological signal estimation approaches. Furthermore, this work highlights the impact of existing data distributions toward controlled laboratory conditions, underscoring their limitations in real world applicability.