Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health Biomarkers Estimation
This addresses the need for diverse, privacy-conscious data to advance AI medical assistants for health monitoring.
The paper tackles the limitations of existing rPPG datasets by introducing a large-scale multi-view video dataset with 3600 recordings from 600 subjects, enabling improved rPPG model training and cross-dataset comparisons.
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.