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

Regulation of Heart Rates01:31

Regulation of Heart Rates

4.0K
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...
4.0K
Factors Influencing Heart Rate01:30

Factors Influencing Heart Rate

6.9K
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,...
6.9K
Passive Filters01:27

Passive Filters

1.0K
Passive filters are utilized to shape the frequency spectrum of signals across a diverse array of applications. These filters, using only passive elements like resistors (R), inductors (L), and capacitors (C), are capable of selectively allowing or blocking certain frequency ranges without the need for external power sources.
Low-Pass Filters
Low-pass filters are designed to transmit signals with frequencies lower than the cutoff frequency, ωc, and attenuate those above it. The cutoff...
1.0K
Cardiac Output I:Effect of Heart Rate on Cardiac Output01:19

Cardiac Output I:Effect of Heart Rate on Cardiac Output

2.8K
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...
2.8K
Active Filters01:25

Active Filters

1.4K
Active filters are electronic circuits that use operational amplifiers (op-amps), resistors, and capacitors to filter out unwanted frequency components from a signal. A first-order low-pass active filter is designed to pass signals with a frequency lower than a certain cutoff frequency and attenuate frequencies higher than that cutoff frequency. The transfer function for a first-order low-pass active filter is:
1.4K
Contact Angle01:13

Contact Angle

24.8K
When a solid is dipped inside a liquid, the liquid surface becomes curved near the contact. For some solid–liquid interfaces, the liquid is pulled up along the solid, while for others, the liquid surface is convex or depressed near the solid surface. This phenomenon can be explained using the concept of cohesive and adhesive forces.
The adhesive force is the molecular force between molecules of different materials, that is, between the molecules of the solid and the liquid. The cohesive...
24.8K

You might also read

Related Articles

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

Sort by
Same author

Video-based estimation of blood pressure.

PloS one·2025
Same author

Video-Based Elevated Skin Temperature Detection.

IEEE transactions on bio-medical engineering·2023
Same author

Evaluation of biases in remote photoplethysmography methods.

NPJ digital medicine·2021
Same author

Classification of unlabeled online media.

Scientific reports·2021
Same author

How are you feeling?: A personalized methodology for predicting mental states from temporally observable physical and behavioral information.

Journal of biomedical informatics·2017
Same author

An unsupervised machine learning model for discovering latent infectious diseases using social media data.

Journal of biomedical informatics·2016

Related Experiment Video

Updated: Feb 13, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

2.2K

Bounded Kalman filter method for motion-robust, non-contact heart rate estimation.

Sakthi Kumar Arul Prakash1, Conrad S Tucker2

  • 1Department of Industrial and Manufacturing Engineering, Pennsylvania State University, State College, Pennsylvania 16801, USA.

Biomedical Optics Express
|March 20, 2018
PubMed
Summary

This study introduces a new remote photoplethysmography (rPPG) algorithm for accurate real-time heart rate measurement. It effectively minimizes motion artifacts, enabling practical use in real-world telehealth applications.

Keywords:
(170.1470) Blood or tissue constituent monitoring(170.3880) Medical and biological imaging(280.4788) Optical sensing and sensors

More Related Videos

Estimating Virus Production Rates in Aquatic Systems
10:49

Estimating Virus Production Rates in Aquatic Systems

Published on: September 22, 2010

13.1K
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

14.1K

Related Experiment Videos

Last Updated: Feb 13, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

2.2K
Estimating Virus Production Rates in Aquatic Systems
10:49

Estimating Virus Production Rates in Aquatic Systems

Published on: September 22, 2010

13.1K
Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations
12:09

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Published on: January 8, 2013

14.1K

Area of Science:

  • Biomedical Engineering
  • Signal Processing
  • Medical Informatics

Background:

  • Existing remote photoplethysmography (rPPG) methods require static environments, limiting practical telehealth applications.
  • Real-world scenarios involve patient motion and variable lighting, posing challenges for accurate heart rate detection.
  • Motion artifacts like blurring and noise degrade the performance of current rPPG techniques.

Purpose of the Study:

  • To develop and validate a novel algorithm for real-time heart rate measurement under diverse lighting and motion conditions.
  • To overcome the limitations of existing rPPG methods in non-controlled environments.
  • To enable robust remote heart rate monitoring for telehealth and mobile health applications.

Main Methods:

  • Implementation of a real-time heart rate measurement algorithm using remote photoplethysmography (rPPG).
  • Integration of a blur identification and denoising algorithm to mitigate frame-specific artifacts.
  • Application of a bounded Kalman filter for motion estimation and feature tracking to reduce noise.
  • Validation through a case study involving subjects performing everyday head and body movements.

Main Results:

  • The proposed algorithm demonstrates effective real-time heart rate measurement across various lighting conditions and motion categories.
  • Significant reduction in motion artifacts, including blurring and noise, was achieved.
  • Benchmarked results show superior performance compared to state-of-the-art rPPG methods in heart rate detection.
  • The case study confirmed the algorithm's feasibility for non-contact pulse rate estimation during natural movements.

Conclusions:

  • The developed rPPG algorithm offers a practical solution for accurate remote heart rate monitoring in real-world settings.
  • The method's robustness to motion artifacts enhances its utility for telehealth and mobile health.
  • This advancement represents a significant improvement over existing rPPG techniques, paving the way for wider clinical adoption.