Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health Biomarkers Estimation

TL;DR

This paper introduces a large-scale, multi-view video dataset with synchronized physiological signals and health metrics to advance remote rPPG and health biomarker estimation, addressing limitations of existing datasets.

Contribution

The paper presents a comprehensive, diverse, multi-view dataset with synchronized physiological and health data, enabling improved AI-based health monitoring and biomarker analysis.

Findings

The dataset includes 3600 synchronized videos from 600 subjects.

An efficient rPPG model trained on this dataset outperforms existing approaches.

Public release of data and models will accelerate AI medical assistant development.

Abstract

Progress in remote PhotoPlethysmoGraphy (rPPG) is limited by the critical issues of existing publicly available datasets: small size, privacy concerns with facial videos, and lack of diversity in conditions. The paper introduces a novel comprehensive large-scale multi-view video dataset for rPPG and health biomarkers estimation. Our dataset comprises 3600 synchronized video recordings from 600 subjects, captured under varied conditions (resting and post-exercise) using multiple consumer-grade cameras at different angles. To enable multimodal analysis of physiological states, each recording is paired with a 100 Hz PPG signal and extended health metrics, such as electrocardiogram, arterial blood pressure, biomarkers, temperature, oxygen saturation, respiratory rate, and stress level. Using this data, we train an efficient rPPG model and compare its quality with existing approaches in cross-dataset scenarios. The public release of our dataset and model should significantly speed up the progress in the development of AI medical assistants.