Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...

Kinematic Equations: Problem Solving

Kinematic Equations: Problem Solving

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...

Kinematic Equations - II

Kinematic Equations - II

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...

Kinematic Equations - I

Kinematic Equations - I

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:

Kinematic Equations - III

Kinematic Equations - III

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,...

Linear Approximation in Time Domain

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...

You might also read

Related Articles

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

Sort by

Same author

Robust monitoring of thermal and fast ions using collective Thomson scattering: Combining physics- and data-driven background estimation.

The Review of scientific instruments·2025

Same author

Co-Occurrence of Sarcopenia and Frailty in Acutely Admitted Older Medical Patients: Results from the Copenhagen PROTECT Study.

The Journal of frailty & aging·2024

Same author

Development of the 174 GHz collective Thomson scattering diagnostics at Wendelstein 7-X.

The Review of scientific instruments·2024

Same author

ITER collective Thomson scattering-Preparing to diagnose fusion-born alpha particles (invited).

The Review of scientific instruments·2022

Same author

Dynamics of microbiotic patterns reveal surface water groundwater interactions in intermittent and perennial streams.

The Science of the total environment·2021

Same author

Patients with knee osteoarthritis can be divided into subgroups based on tibiofemoral joint kinematics of gait - an exploratory and dynamic radiostereometric study.

Osteoarthritis and cartilage·2021

Search research articles

Related Experiment Video

Updated: Jun 20, 2026

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

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

Published on: April 11, 2018

A computationally efficient optimisation-based method for parameter identification of kinematically determinate and

M S Andersen¹, M Damsgaard, B MacWilliams

¹Department of Mechanical Engineering, Aalborg University, Pontoppidanstraede 101, DK-9220 Aalborg East, Denmark. msa@me.aau.dk

Computer Methods in Biomechanics and Biomedical Engineering

|August 21, 2009

Summary

This study presents an efficient optimization method to identify biomechanical parameters from motion data. The approach accurately determines system constants and time-varying coordinates, crucial for biomechanical motion analysis.

More Related Videos

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

Published on: May 8, 2014

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

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

Related Experiment Videos

Last Updated: Jun 20, 2026

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

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

Published on: April 11, 2018

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

Published on: May 8, 2014

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

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

Area of Science:

Biomechanics
Optimization Methods
Motion Analysis

Background:

Accurate determination of biomechanical parameters is challenging in motion analysis.
Existing methods may struggle with complex kinematic systems.
Direct measurements often yield inaccuracies in system parameters.

Purpose of the Study:

To introduce a general optimization-based method for identifying biomechanically relevant parameters.
To address the challenge of finding constant parameters and time-varying coordinates from motion data.
To provide an efficient solution for parameter identification in biomechanical systems.

Main Methods:

Developed a general optimization-based framework.
Utilized the structure of linearized Karush–Kuhn–Tucker optimality conditions for efficiency.
Applied the method to kinematically determinate and over-determinate systems.

Main Results:

Successfully identified biomechanically relevant parameters from motion data.
Demonstrated efficient computation due to the special structure of optimality conditions.
Validated the method on test cases relevant to gait analysis.

Conclusions:

The proposed optimization method offers an efficient way to identify biomechanical parameters.
The technique is applicable to determining marker coordinates, segment lengths, and joint axes.
This approach is highly relevant for accurate biomechanical motion analysis, particularly in gait studies.