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

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

863
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
863
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

683
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...
683
Kinematic Equations for Rotation01:30

Kinematic Equations for Rotation

726
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...
726
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

730
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame. The absolute velocity of point B is determined by adding the absolute velocity of point A, the relative velocity of point B in the rotating frame, and the effects caused by the angular velocity within the rotating frame.
Time differentiation is...
730
Inertial Frames of Reference01:03

Inertial Frames of Reference

8.6K
Newton’s first law is usually considered to be a statement about reference frames. It provides a method for identifying a special type of reference frame: the inertial reference frame. In principle, we can make the net force on a body zero. If its velocity relative to a given frame is constant, then that frame is said to be inertial. So, by definition, an inertial reference frame is a reference frame where Newton's first law holds valid. Newton's first law applies to objects with...
8.6K
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

508
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
508

You might also read

Related Articles

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

Sort by
Same author

High-fidelity but hypometric spatial localization of afterimages across saccades.

Science advances·2026
Same author

Optimizing the load capacity of a compliant-mechanism-based microplate gripper for biomedical lab automation.

Biomedizinische Technik. Biomedical engineering·2026
Same author

Predicting gait kinetics using 3-degrees of freedom acceleration data and artificial neural networks.

Clinical biomechanics (Bristol, Avon)·2026
Same author

Camera-based In-Process Evaluation and Parameter Study of Core-Shell-Capsule Dimensions.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Early visual signatures and benefits of intra-saccadic motion streaks.

PLoS computational biology·2025
Same author

DIODEM - A Diverse Inertial and Optical Dataset of kinEmatic chain Motion.

Scientific data·2025

Related Experiment Video

Updated: Jan 9, 2026

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

8.4K

A Plug-and-Play Inertial Motion Tracking Method for Magnetometer-free Orientation Estimation of Arbitrary Joints.

Timo Kuhlgatz, Simon Bachhuber, Thomas Seel

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |December 3, 2025
    PubMed
    Summary

    This study introduces a novel recurrent neural network model for inertial motion tracking of kinematic chains. The plug-and-play system simplifies motion analysis by not requiring prior joint knowledge, making it accessible for non-experts.

    More Related Videos

    Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane
    07:24

    Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

    Published on: August 22, 2025

    455
    Magnetic Tweezers for the Measurement of Twist and Torque
    11:41

    Magnetic Tweezers for the Measurement of Twist and Torque

    Published on: May 19, 2014

    23.8K

    Related Experiment Videos

    Last Updated: Jan 9, 2026

    An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
    06:52

    An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

    Published on: May 26, 2020

    8.4K
    Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane
    07:24

    Using Eye-tracking to Assess the Relative Importance of Visual and Vestibular Input to Subcortical Motion Processing in the Roll Plane

    Published on: August 22, 2025

    455
    Magnetic Tweezers for the Measurement of Twist and Torque
    11:41

    Magnetic Tweezers for the Measurement of Twist and Torque

    Published on: May 19, 2014

    23.8K

    Area of Science:

    • Robotics
    • Biomechanics
    • Machine Learning

    Background:

    • Inertial motion tracking (IMT) is crucial for analyzing human and robot movement.
    • Current IMT methods for kinematic chains (KC) demand expert knowledge of joint degrees of freedom (DoF) and axes, limiting accessibility.
    • There is a need for simplified, adaptable IMT-KC solutions for broader applications.

    Purpose of the Study:

    • To develop a plug-and-play recurrent neural network (RNN)-based model for IMT-KC.
    • To estimate relative orientation between adjacent inertial measurement units (IMUs) without prior knowledge of joint axes or DoF.
    • To enhance the accessibility and deployment of IMT-KC systems.

    Main Methods:

    • Developed an RNN-based model for IMT-KC.
    • Trained the model exclusively on simulated data.
    • Evaluated the model on real-world 3D-printed kinematic chains with varying DoF.

    Main Results:

    • The model achieved zero-shot estimation of IMU relative orientation on real-world KCs.
    • Demonstrated a mean error of less than 9° across all joint types (1, 2, and 3 DoF).
    • Showcased model stability, resilience to IMU placement variations, and flexibility.

    Conclusions:

    • The proposed RNN model significantly simplifies IMT-KC deployment by reducing the need for expert knowledge.
    • The system offers flexibility, robustness, and ease of use, making IMT-KC more accessible to non-experts.
    • This advancement facilitates applications in biomedical fields like rehabilitation and everyday motion analysis outside controlled environments.