VideoPulse: Neonatal heart rate and peripheral capillary oxygen saturation (SpO2) estimation from contact free video

• URL: http://arxiv.org/abs/2602.23771v1
• Date: Fri, 27 Feb 2026 07:57:04 GMT
• Title: VideoPulse: Neonatal heart rate and peripheral capillary oxygen saturation (SpO2) estimation from contact free video
• Authors: Deependra Dewagiri, Kamesh Anuradha, Pabadhi Liyanage, Helitha Kulatunga, Pamuditha Somarathne, Udaya S. K. P. Miriya Thanthrige, Nishani Lucas, Anusha Withana, Joshua P. Kulasingham,
• Abstract summary: VideoPulse contains 157 recordings totaling 2.6 hours from 52 neonates with diverse face orientations.<n>Our pipeline performs face alignment and artifact aware supervision using denoised pulse oximeter signals.<n>On the NBHR neonatal dataset, we obtain heart rate MAE 2.97 bpm using 2 second windows and SpO2 MAE 1.69 percent.
• Score: 1.8148696158263362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Overcoming Small Data Limitations in Video-Based Infant Respiration Estimation [7.723650415926449]
  We introduce the annotated infant respiration dataset of 400 videos (AIR-400), contributing 275 new annotated videos from 10 recruited subjects to the public corpus.<n>We develop the first reproducible pipelines for infant respiration estimation, based on infant-specific region-of-interest detection andtemporal neural processing enhanced by optical flow inputs.<n>We establish, through comprehensive experiments, the first reproducible benchmarks for the state-of-the-art in vision-based infant respiration estimation.
  arXiv  Detail & Related papers  (2025-12-07T15:25:17Z)
• Prototyping an End-to-End Multi-Modal Tiny-CNN for Cardiovascular Sensor Patches [35.899401205042615]
  We propose a convolutional neural network with early fusion of data to solve a binary classification problem.<n>Our approach reduces memory footprint and compute cost by three orders of magnitude compared to the state-of-the-art.
  arXiv  Detail & Related papers  (2025-10-21T14:23:20Z)
• Gaze into the Heart: A Multi-View Video Dataset for rPPG and Health Biomarkers Estimation [36.002060195915526]
  The paper introduces a novel large-scale multi-view video dataset for r and health estimation.<n>Our dataset comprises synchronized video recordings from 600 subjects, captured under varied conditions.<n>The public release of our dataset and model should significantly speed up the progress in the development of AI medical assistants.
  arXiv  Detail & Related papers  (2025-08-25T11:46:40Z)
• Recovering Pulse Waves from Video Using Deep Unrolling and Deep Equilibrium Models [45.94962431110573]
  Camera-based monitoring of vital signs, also known as imaging photoplethysmography (i), has seen applications in driver-monitoring, affective computing, and more.<n>We introduce methods that combine signal processing and deep learning methods in an inverse problem.
  arXiv  Detail & Related papers  (2025-03-21T16:11:21Z)
• Two-Stream Thermal Imaging Fusion for Enhanced Time of Birth Detection in Neonatal Care [1.101731711817642]
  We present a two-stream fusion system that combines the power of image and video analysis to accurately detect the Time of Birth (ToB)<n>Our system achieves 95.7% precision and 84.8% recall in detecting birth within short video clips.
  arXiv  Detail & Related papers  (2025-03-05T07:52:52Z)
• Summit Vitals: Multi-Camera and Multi-Signal Biosensing at High Altitudes [22.23531900474421]
  Video photoplethysmography is an emerging method for non-invasive and convenient measurement of physiological signals. This dataset is designed to validate video vitals estimation algorithms and fusing videos from different positions. Our findings suggest that simultaneous training on multiple indicators, such as PPG and blood oxygen, can reduce MAE in SpO2 estimation by 17.8%.
  arXiv  Detail & Related papers  (2024-09-28T03:36:16Z)
• Vision-language models for decoding provider attention during neonatal resuscitation [33.7054351451505]
  We introduce an automated, real-time, deep learning approach capable of decoding provider gaze into semantic classes. Our pipeline attains 91% classification accuracy in identifying gaze targets without training. Our approach offers a scalable solution that seamlessly integrates with existing infrastructure for data-scarce gaze analysis.
  arXiv  Detail & Related papers  (2024-04-01T16:09:12Z)
• Automatic Infant Respiration Estimation from Video: A Deep Flow-based Algorithm and a Novel Public Benchmark [10.097634735211654]
  We develop a deep-learning method for estimating respiratory rate and waveform from plain video footage in natural settings. Our model supports the first public annotated infant respiration dataset with 125 videos. When trained and tested on the AIR-125 infant data, our method significantly outperforms other state-of-the-art methods in respiratory rate estimation.
  arXiv  Detail & Related papers  (2023-07-24T19:59:15Z)
• Using BOLD-fMRI to Compute the Respiration Volume per Time (RTV) and Respiration Variation (RV) with Convolutional Neural Networks (CNN) in the Human Connectome Development Cohort [55.41644538483948]
  This study proposes a one-dimensional CNN model for reconstruction of two respiratory measures, RV and RVT. Results show that a CNN can capture informative features from resting BOLD signals and reconstruct realistic RV and RVT timeseries.
  arXiv  Detail & Related papers  (2023-07-03T18:06:36Z)
• ReViSe: Remote Vital Signs Measurement Using Smartphone Camera [0.0]
  Remote Photoplethysmography (rVi) is a fast, effective, inexpensive and convenient method for collecting biometric data. We propose an end-to-end framework to measure people's vital signs based on the ruration of a user's face captured with a smartphone camera. We extract face landmarks with a deep learning-based neural network model in real-time.
  arXiv  Detail & Related papers  (2022-06-13T19:20:11Z)
• A Supervised Learning Approach for Robust Health Monitoring using Face Videos [32.157163136267954]
  Non-contact, device-free human sensing methods can eliminate the need for specialized heart and blood pressure monitoring equipment. In this paper, we used a non-contact method that only requires face videos recorded using commercially-available webcams. The proposed approach used facial recognition to detect the face in each frame of the video using facial landmarks.
  arXiv  Detail & Related papers  (2021-01-30T22:03:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.