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

One-Degree-of-Freedom System01:24

One-Degree-of-Freedom System

466
In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
A one-degree-of-freedom system is defined by an independent variable that determines its state and behavior. One example of a one-degree-of-freedom system is a simple harmonic oscillator, such as a...
466
Kinematic Equations - III01:18

Kinematic Equations - III

7.6K
The first two kinematic equations have time as a variable, but the third kinematic equation is independent of time. This equation expresses final velocity as a function of the acceleration and distance over which it acts. The fourth kinematic equation does not have an acceleration term and provides the final position of the object at time t in terms of the initial and final velocities. This equation is useful when the value of the constant acceleration is unknown.
Using the kinematic equations,...
7.6K
Kinematic Equations - II01:17

Kinematic Equations - II

9.4K
The second kinematic equation expresses the final position of an object in terms of its initial position, the distance traveled with the initial constant velocity, and the distance traveled due to a change in velocity. Similar to the first kinematic equation, this equation is also only valid when the acceleration is constant throughout the motion of an object.
Suppose a car merges into freeway traffic on a 200 m long ramp. If its initial velocity is 10 m/s and it accelerates at 2 m/s2, then the...
9.4K
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

390
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
390
Kinematic Equations for Rotation01:30

Kinematic Equations for Rotation

317
In mechanics, when one observes a rigid body in rotational motion with constant angular acceleration, it is possible to establish equations for its rotational kinematics. This process resembles how linear kinematics are dealt with in simpler motion studies.
For instance, imagine a point A on a rigid body engaged in circular motion. The translational velocity of this particular point can be calculated by taking the time derivatives of the displacement equation, which essentially measures the...
317
Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

12.0K
When analyzing one-dimensional motion with constant acceleration, the problem-solving strategy involves identifying the known quantities and choosing the appropriate kinematic equations to solve for the unknowns. Either one or two kinematic equations are needed to solve for the unknowns, depending on the known and unknown quantities. Generally, the number of equations required is the same as the number of unknown quantities in the given example. Two-body pursuit problems always require two...
12.0K

You might also read

Related Articles

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

Sort by
Same author

Three-phase microenvironment modification by optimizing ionomer towards high-performance proton exchange membrane fuel cells.

Chemical Society reviews·2025
Same author

Three-degrees-of-freedom orientation manipulation of small untethered robots with a single anisotropic soft magnet.

Nature communications·2023
Same author

Covalent organic framework-based porous ionomers for high-performance fuel cells.

Science (New York, N.Y.)·2022
See all related articles

Related Experiment Video

Updated: Jun 11, 2025

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.4K

A Robust Tri-Electromagnet-Based 6-DoF Pose Tracking System Using an Error-State Kalman Filter.

Shuda Dong1, Heng Wang1

  • 1Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou 511442, China.

Sensors (Basel, Switzerland)
|September 28, 2024
PubMed
Summary

This study introduces an error-state Kalman filter (ESKF) for magnetic pose tracking, improving accuracy in medical interventions. The ESKF effectively maintains quaternion unity, enhancing the precision of tracking medical devices like endoscopes.

Keywords:
Kalman filterelectromagnetic trackinglocalizationmagnetic sensorpose estimation

More Related Videos

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K
A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

14.8K

Related Experiment Videos

Last Updated: Jun 11, 2025

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.4K
Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K
A Protocol for Real-time 3D Single Particle Tracking
10:16

A Protocol for Real-time 3D Single Particle Tracking

Published on: January 3, 2018

14.8K

Area of Science:

  • Medical instrumentation
  • Robotics and Control Systems
  • Biomedical Engineering

Background:

  • Magnetic pose tracking offers non-contact, occlusion-free measurement for intra-corporeal medical devices.
  • Existing algorithms like the Extended Kalman Filter (EKF) struggle with quaternion unity constraints, impacting accuracy.
  • Accurate pose estimation is critical for computer-assisted medical interventions.

Purpose of the Study:

  • To propose an enhanced pose estimation algorithm for electromagnetic tracking systems.
  • To improve the accuracy and robustness of magnetic pose tracking.
  • To address the limitations of existing algorithms in maintaining orientation constraints.

Main Methods:

  • Development of an error-state Kalman filter (ESKF) algorithm for pose estimation.
  • Implementation of a system with three electromagnetic coils and a tri-axial magnetic sensor.
  • Utilizing sequential coil excitation for magnetic field separation and disturbance rejection.
  • Comparison of ESKF with Extended Kalman Filter (EKF) and constrained EKF through simulations and experiments.

Main Results:

  • The ESKF algorithm effectively maintains the unity constraint of quaternions.
  • Achieved a Euclidean position error of 2.23 mm and an average orientation angle error of 0.45°.
  • Demonstrated superior tracking accuracy and robustness compared to EKF and constrained EKF.
  • Experimentally validated the system's disturbance rejection capabilities.

Conclusions:

  • The proposed ESKF algorithm significantly enhances the accuracy and robustness of magnetic pose tracking systems.
  • ESKF provides a more reliable solution for tracking medical devices in computer-assisted interventions.
  • This method overcomes limitations of traditional algorithms, ensuring better performance in demanding applications.