Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: May 14, 2026

Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

Combining Video Magnification with Machine Learning-Based Source Identification for Contactless Heart Rate

Tiago de Avelar1,2, Vicente M Garção1,2,3, Hugo Plácido da Silva1,2

  • 1Department of Bioengineering, Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisboa, Portugal.

Sensors (Basel, Switzerland)
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Are Reusable Dry Electrodes an Alternative to Gelled Electrodes for Canine Surface Electromyography?

Animals : an open access journal from MDPI·2025
Same author

MindRegulation-SEL: randomized controlled trial of the effects of a relaxation and guided imagery intervention with socioemotional learning on the psychological and biophysiological well-being, socioemotional development, cognitive function and academic achievement of elementary school children.

Trials·2025
Same author

Electrodermal Activity Analysis at Different Body Locations.

Sensors (Basel, Switzerland)·2025
Same author

Design and Evaluation of a Novel Venturi-Based Spirometer for Home Respiratory Monitoring.

Sensors (Basel, Switzerland)·2024
Same author

Automated algorithms for seizure forecast: a systematic review and meta-analysis.

Journal of neurology·2024
Same author

Validation of an mHealth System for Monitoring Fundamental Physiological Parameters in the Clinical Setting.

Sensors (Basel, Switzerland)·2024
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Contactless heart rate (HR) monitoring using facial video offers a comfortable alternative to traditional methods. This study presents a hybrid framework achieving accurate HR estimation, outperforming existing algorithms.

Area of Science:

  • Biomedical Engineering
  • Computer Science
  • Signal Processing

Background:

  • Conventional heart rate (HR) monitoring methods face challenges including patient discomfort, skin irritation, and poor adherence.
  • Contactless sensing systems are needed to overcome limitations of traditional HR monitoring.

Purpose of the Study:

  • To develop and validate a robust hybrid framework for accurate contactless heart rate estimation from facial video.
  • To enhance HR estimation accuracy by combining advanced signal processing and machine learning techniques.

Main Methods:

  • A two-stage geometric stabilization pipeline with dense facial tessellation was used to mitigate motion artifacts.
  • Eulerian Video Magnification (EVM) and chrominance-based Region of Interest (ROI) filtering were employed for signal enhancement.
Keywords:
blind source separationcomputer visioncontactless monitoringheart ratemachine learning

More Related Videos

Automatic Surgery in Transcatheter Aortic Valve Replacement Using Augmented Reality
07:46

Automatic Surgery in Transcatheter Aortic Valve Replacement Using Augmented Reality

Published on: August 9, 2024

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Related Experiment Videos

Last Updated: May 14, 2026

Semi-automated Optical Heartbeat Analysis of Small Hearts
12:10

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

Automatic Surgery in Transcatheter Aortic Valve Replacement Using Augmented Reality
07:46

Automatic Surgery in Transcatheter Aortic Valve Replacement Using Augmented Reality

Published on: August 9, 2024

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

  • Signal recovery involved sliding-window Principal Component Analysis (PCA) and Second-Order Blind Identification (SOBI), with a Light Gradient Boosting Machine (LightGBM) classifier for physiological source identification.

    • Main Results:

    • The framework achieved a Mean Absolute Error (MAE) of 1.50 bpm, Root Mean Square Error (RMSE) of 3.07 bpm, and a Pearson Correlation Coefficient (PCC) of 0.97 on the COHFACE dataset.
    • The method demonstrated robustness across diverse lighting conditions.
    • Performance was comparable to state-of-the-art deep learning models and superior to traditional algorithms.

    Conclusions:

    • The proposed hybrid framework offers an accurate and robust solution for contactless heart rate monitoring using facial video.
    • This interpretable approach provides a viable alternative for long-term, non-invasive health monitoring.
    • The study highlights the potential of advanced signal processing and machine learning in remote physiological sensing.