Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health
  Biomarkers Estimation
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.
68b01730245176306494cd40
Konstantin Egorov
68b01730245176306494cd41
Stepan Botman
68b01730245176306494cd42
Pavel Blinov
claimed_verified
2025-08-28T08:49:45.516Z
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/avatars/e9bf7fe8a637b2869d04649cd93dfc78.svg
Pavel Blinov
user
blinoff
68b01730245176306494cd43
Galina Zubkova
68b01730245176306494cd44
Anton Ivaschenko
68b01730245176306494cd45
Alexander Kolsanov
68b01730245176306494cd46
Andrey Savchenko
Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health
  Biomarkers Estimation
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.
A large-scale multi-view video dataset for rPPG and health biomarkers estimation is introduced, enabling efficient rPPG model training and comparison with existing approaches.
rPPG multi-view video dataset PPG signal electrocardiogram arterial blood pressure biomarkers temperature oxygen saturation respiratory rate stress level