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

Kinematic Equations - II01:17

Kinematic Equations - II

10.8K
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...
10.8K
Kinematic Equations - I01:26

Kinematic Equations - I

12.0K
When an object moves with constant acceleration, the velocity of the object changes at a constant rate throughout the motion. The kinematic equations of motions are derived for such cases where the acceleration of the object is constant. The first kinematic equation gives an insight into the relationship between velocity, acceleration, and time. We can see, for example:
12.0K
Kinematic Equations: Problem Solving01:15

Kinematic Equations: Problem Solving

14.6K
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...
14.6K
Kinematic Equations - III01:18

Kinematic Equations - III

8.6K
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,...
8.6K
Kinematic Equations for Rotation01:30

Kinematic Equations for Rotation

375
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...
375
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

536
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...
536

You might also read

Related Articles

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

Sort by
Same author

Coil Orientation in Transcranial Magnetic Stimulation Affects Motor-evoked Potential Size more than its Timing or Waveform Shape.

Brain topography·2026
Same author

Neural Network-Driven Finite Element Modeling for Estimating Knee Joint Cartilage Mechanical Responses.

Annals of biomedical engineering·2026
Same author

Digital Assistive Technology Acceptance and Use by Caregivers of Older Adults With Cognitive Impairment: Qualitative Interview Study.

JMIR human factors·2026
Same author

OMEGA V2: GPU-accelerated Python and MATLAB software for PET, SPECT, and CT reconstruction.

Physics in medicine and biology·2026
Same author

Generating synthetic images of human skeletal motion for pose and kinematics estimation tasks.

Scientific data·2025
Same author

Evaluation of visual ergonomics in microsurgery: a real-time video processing solution.

Acta neurochirurgica·2025

Related Experiment Video

Updated: Sep 13, 2025

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

9.8K

Musculoskeletal Inverse Kinematics Tool for Inertial Motion Capture Data Based on the Adaptive Unscented Kalman

Matti J Kortelainen1, Paavo Vartiainen2, Alexander Beattie2

  • 1Department of Technical Physics, University of Eastern Finland, P.O. Box 1627, 70211, Kuopio, Finland. matti.kortelainen@uef.fi.

Annals of Biomedical Engineering
|July 28, 2025
PubMed
Summary

AUKSMIKT, a new Bayesian tool, improves human whole-body kinematics estimation from motion capture data. It offers comparable or higher accuracy than existing methods, addressing limitations in noise and uncertainty estimation.

Keywords:
Inertial sensorsKalman filtersMotion estimationOpen source software

More Related Videos

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

8.1K
In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
07:43

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

3.2K

Related Experiment Videos

Last Updated: Sep 13, 2025

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
09:32

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

9.8K
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

8.1K
In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
07:43

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

Published on: July 2, 2021

3.2K

Area of Science:

  • Biomechanics
  • Motion Analysis
  • Computational Physiology

Background:

  • Conventional human kinematics estimation tools assume uncorrelated, zero-mean Gaussian noise.
  • These tools lack the ability to estimate solution uncertainty.
  • There is a need for advanced methods to handle noise and uncertainty in motion capture data.

Purpose of the Study:

  • To introduce AUKSMIKT, a novel tool for whole-body human kinematics estimation.
  • To implement AUKSMIKT within the Bayesian framework to account for noise and uncertainty.
  • To compare AUKSMIKT's performance against conventional least squares estimation methods.

Main Methods:

  • AUKSMIKT was implemented as a C++ class extending the OpenSim API.
  • It utilizes an unscented Kalman filter with run-time noise estimation and a fixed-lag Rauch-Tung-Striebel smoother.
  • Performance was evaluated using a public dataset of optical and inertial motion capture data during overground walking.

Main Results:

  • AUKSMIKT demonstrated reduced mean absolute errors for angular positions, velocities, and accelerations in several joints compared to the native OpenSim tool.
  • Specific improvements were noted for angular position (3 joints), velocities (6 joints), and accelerations (7 joints).
  • Slightly larger errors were observed for angular positions (5 joints) and velocities (3 joints) in AUKSMIKT.

Conclusions:

  • AUKSMIKT provides comparable or superior accuracy for lower-body kinematics estimation from inertial motion capture data.
  • The Bayesian approach in AUKSMIKT effectively addresses limitations of conventional methods.
  • AUKSMIKT represents a significant advancement in human motion analysis, particularly for processing noisy inertial sensor data.