VideoPulse: Neonatal heart rate and peripheral capillary oxygen saturation (SpO2) estimation from contact free video
Not on the map. Why?
Abstract (arXiv)
Remote photoplethysmography (rPPG) enables contact free monitoring of vital signs and is especially valuable for neonates, since conventional methods often require sustained skin contact with adhesive probes that can irritate fragile skin and increase infection control burden. We present VideoPulse, a neonatal dataset and an end to end pipeline that estimates neonatal heart rate and peripheral capillary oxygen saturation (SpO2) from facial video. VideoPulse contains 157 recordings totaling 2.6 hours from 52 neonates with diverse face orientations. Our pipeline performs face alignment and artifact aware supervision using denoised pulse oximeter signals, then applies 3D CNN backbones for heart rate and SpO2 regression with label distribution smoothing and weighted regression for SpO2. Predictions are produced in 2 second windows. On the NBHR neonatal dataset, we obtain heart rate MAE 2.97 bpm using 2 second windows (2.80 bpm at 6 second windows) and SpO2 MAE 1.69 percent. Under cross dataset evaluation, the NBHR trained heart rate model attains 5.34 bpm MAE on VideoPulse, and fine tuning an NBHR pretrained SpO2 model on VideoPulse yields MAE 1.68 percent. These results indicate that short unaligned neonatal video segments can support accurate heart rate and SpO2 estimation, enabling low cost non invasive monitoring in neonatal intensive care.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 14 of its 48 references that are in the map. First posted on arXiv 27 Feb 2026.
Builds on
14 of 48 references.
References · 14
- 2023Mamba: Linear-Time Sequence Modeling with Selective State Spacescited by 4,616
- 2021Delving into Deep Imbalanced Regressioncited by 157
- 2018DeepPhys: Video-Based Physiological Measurement Using Convolutional Attention Networkscited by 153
- 2020Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurementcited by 133
- 2019Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networkscited by 126
- 2021PhysFormer: Facial Video-based Physiological Measurement with Temporal Difference Transformercited by 118
- 2021EfficientPhys: Enabling Simple, Fast and Accurate Camera-Based Vitals Measurementcited by 71
- 2022rPPG-Toolbox: Deep Remote PPG Toolboxcited by 63
- 2018VIPL-HR: A Multi-modal Database for Pulse Estimation from Less-constrained Face Videocited by 57
- 2020AutoHR: A Strong End-to-end Baseline for Remote Heart Rate Measurement with Neural Searchingcited by 39
- 2023Non-Contrastive Unsupervised Learning of Physiological Signals from Videocited by 37
- 2024RhythmMamba: Fast, Lightweight, and Accurate Remote Physiological Measurementcited by 20
2 more
Built on by
Not cited yet.