构建大规模多视角视频数据集，助力无接触生理信号测量。
Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health Biomarkers Estimation
- 采集600人、3600段多角度视频，覆盖静息与运动状态。
- 每段视频配100Hz PPG及心电、血压等10余项健康指标。
- 开源数据集和模型，推动AI医疗助手发展。
rPPG研究受限于现有公开数据集的规模小、隐私风险高及条件单一等问题。本文提出一个大规模多视角视频数据集，用于rPPG与健康生物标志物估计。数据集包含600名受试者的3600段同步视频，采用多种消费级相机在不同角度下采集，涵盖静息与运动状态。每段视频均配有100 Hz PPG信号及心电图、动脉血压、血氧饱和度、呼吸率、体温、应激水平等扩展健康指标。基于该数据集训练高效rPPG模型，并在跨数据集场景下与现有方法对比性能。数据集与模型将公开发布，显著促进AI医疗助手的发展。
原文摘要 · Abstract (English)
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.
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