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 Concept Videos

Factors Influencing Heart Rate01:30

Factors Influencing Heart Rate

7.4K
The heart rate, or pulse rate, is a vital indicator of cardiovascular health. It reflects the number of times the heart beats per minute. Various physiological and environmental factors influence heart rate, increasing or decreasing cardiac output. Understanding these factors is crucial for assessing heart function and identifying potential health issues.
Let us explore the significant factors affecting heart rate, including age, body temperature, posture, acute pain, chemical influences,...
7.4K
Pulse rhythm01:30

Pulse rhythm

1.7K
Pulse rhythm refers to the pattern of pulsations within specific intervals, offering valuable insights into the regularity or irregularity of the heart's beats as observed through the pattern of pulsation within specific intervals. A regular pulse exhibits a consistent heart rate with uniform waveforms and pulsation force, variations of which can be classified as normal, weak, or bounding.
Conversely, an irregular pulse pattern is termed dysrhythmia, stemming from disruptions in cardiac...
1.7K
Special considerations while measuring pulse01:13

Special considerations while measuring pulse

1.1K
Assessing a patient's pulse is a fundamental skill in healthcare, but certain situations require special attention:
1.1K
Regulation of Heart Rates01:31

Regulation of Heart Rates

5.0K
The regulation of heart rate is a complex process controlled by the autonomic nervous system (ANS), hormonal influences, and intrinsic cardiac mechanisms. The ANS has two main components: the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS).
The SNS increases heart rate through the release of norepinephrine and epinephrine, which act on beta-1 adrenergic receptors in the heart. This action increases the rate of depolarization in the sinoatrial (SA) node, the heart's...
5.0K
Cardiac Output I:Effect of Heart Rate on Cardiac Output01:19

Cardiac Output I:Effect of Heart Rate on Cardiac Output

3.4K
Cardiac Output
Cardiac output (CO) refers to the total amount of blood ejected by one of the ventricles in liters per minute (L/min). In a resting adult, CO ranges from 5 to 6 L/min, adjusting according to the body's metabolic requirements.
Effect of Heart Rate on Cardiac Output
Cardiac output adapts to metabolic demands during stress, physical activity, or illness. The autonomic nervous system regulates heart rate via the sinoatrial node. The parasympathetic nervous system decreases heart...
3.4K

You might also read

Related Articles

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

Sort by
Same author

The Ten-Year Risk Prediction for Cardiovascular Disease for Malaysian Adults Using the Laboratory-Based and Office-Based (Globorisk) Prediction Model.

Medicina (Kaunas, Lithuania)·2022
Same author

Tchebichef moment based restoration of Gaussian blurred images.

Applied optics·2016
Same author

Dynamic heart rate estimation using principal component analysis.

Biomedical optics express·2015
Same author

Online temporally consistent indoor depth video enhancement via static structure.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2015
Same author

VEP optimal channel selection using genetic algorithm for neural network classification of alcoholics.

IEEE transactions on neural networks·2008
Same author

Variable selection using genetic algorithm for analysis of near-infrared spectral data using partial least squares.

Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference·2007
Same journal

Generalizable framework for multi-site bone density prediction using non-dominant wrist optical biomarkers.

Biomedical optics express·2026
Same journal

Erratum: Review of dynamic optical coherence tomography for intracellular motility [Invited]: errata.

Biomedical optics express·2026
Same journal

Digital-micromirror-device-based illumination strategies for background suppression in single-molecule localization microscopy.

Biomedical optics express·2026
Same journal

Synergistic combination of convective self-assembly and hollow core fiber for sensitive SERS detection of glucose molecules.

Biomedical optics express·2026
Same journal

Multimodal diagnostic network integrating infrared and mass spectra for lung cancer.

Biomedical optics express·2026
Same journal

Multimodal Optical Biosensing for Precision Medicine and Healthcare: Introduction to the feature issue.

Biomedical optics express·2026
See all related articles

Related Experiment Video

Updated: Apr 6, 2026

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

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.9K

Dynamic heart rate measurements from video sequences.

Yong-Poh Yu1, P Raveendran2, Chern-Loon Lim3

  • 1Department of Electrical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia.

Biomedical Optics Express
|July 24, 2015
PubMed
Summary
This summary is machine-generated.

This study demonstrates that dynamic heart rate can be accurately measured from video sequences using independent component analysis and mutual information. This non-contact method achieves high accuracy, comparable to traditional heart rate monitors.

Keywords:
(100.0100) Image processing(100.2960) Image analysis(110.4155) Multiframe image processing

More Related Videos

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
08:12

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

Published on: June 5, 2019

20.7K
Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

1.3K

Related Experiment Videos

Last Updated: Apr 6, 2026

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

Semi-automated Optical Heartbeat Analysis of Small Hearts

Published on: September 16, 2009

12.9K
Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions
08:12

Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

Published on: June 5, 2019

20.7K
Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver
14:28

Software for Analysis of Heart Rate and Blood Pressure Time-series Data from the Valsalva Maneuver

Published on: June 27, 2025

1.3K

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Physiological Monitoring

Background:

  • Traditional heart rate monitoring relies on contact sensors.
  • Dynamic heart rate variations are crucial for assessing physiological responses.
  • Remote sensing of vital signs is an emerging area in healthcare.

Purpose of the Study:

  • To investigate the feasibility of measuring dynamic heart rate from video sequences.
  • To develop and validate a non-contact method for heart rate monitoring.
  • To assess the accuracy of video-based heart rate measurement against a contact sensor.

Main Methods:

  • Facial images were captured using a video camera during two experiments involving increasing and decreasing heart rates.
  • Independent Component Analysis (ICA) and mutual information were combined to extract heart rate signals from video data.
  • Heart rate was simultaneously measured using a Polar heart rate monitor for comparison.

Main Results:

  • The proposed method accurately captured dynamic heart rate changes in subjects during cycling.
  • Experimental results showed a root mean square error (RMSE) of 1.88 beats per minute (BPM).
  • A high correlation coefficient of 0.99 was achieved between the video-based method and the reference monitor.

Conclusions:

  • Dynamic heart rate can be reliably measured from video sequences using the proposed ICA and mutual information approach.
  • This non-contact method offers a viable alternative to traditional heart rate sensors.
  • The technique shows potential for remote and unobtrusive physiological monitoring.