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Related Concept Videos

Kinematic Equations - II01:17

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 - III01:18

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,...
Kinematic Equations: Problem Solving01:15

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 - I01:26

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 for Rotation01:30

Kinematic Equations for Rotation

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...
Kinetic Energy for a Rigid Body01:13

Kinetic Energy for a Rigid Body

Imagine a solid object involved in a general planar movement, with its center of mass pinpointed at a spot labeled G. The object's kinetic energy relative to an arbitrary point A can be quantified for each of its particles - the ith particle in this case. This measurement is achieved through the employment of the relative velocity definition. The position vector, known as rA, extends from point A to the mass element i.

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Related Experiment Video

Updated: Jun 27, 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

A Tightly Coupled Multibody Dynamics and Multi-Sensor Fusion Algorithm for Simultaneous Kinematics and Kinetics

Hassan Osman1, Daan de Kanter1, Jelle Boelens1,2

  • 1Department of Biomechanical Engineering, Delft University of Technology, 2628 CD Delft, The Netherlands.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for accurate motion capture using Inertial Measurement Units (IMUs) by integrating sensor data with dynamic models. The approach enhances joint kinematics and kinetics estimation for mobility disorder diagnosis and rehabilitation.

Keywords:
IMUinertial sensorsiterated extended Kalman filter (IEKF)kinematicskineticsmotion capturemovement trackingsensor fusion

Related Experiment Videos

Last Updated: Jun 27, 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

Area of Science:

  • Biomechanics
  • Robotics
  • Sensor Fusion

Background:

  • Inertial Measurement Units (IMUs) offer portable motion capture but face challenges like magnetic distortion and integration drift.
  • Accurate estimation of kinematics and kinetics is crucial for diagnosing mobility disorders and guiding rehabilitation.

Purpose of the Study:

  • To develop a tightly coupled motion-capture approach integrating IMU data with multibody dynamic models.
  • To simultaneously estimate system kinematics and kinetics using only accelerometer and gyroscope data.

Main Methods:

  • Implemented an iterated extended Kalman filter to fuse IMU measurements with multibody dynamic models.
  • Enforced complete multibody system dynamics to improve estimation accuracy.
  • Validated the approach using pendulum and collaborative robot systems with ground-truth data.

Main Results:

  • Achieved a maximum joint angle root-mean-square difference of 3.75° for a pendulum and 3.24° for a robot.
  • Demonstrated accurate kinetic estimates with a maximum joint torque root-mean-square difference of 3.02 Nm (pendulum) and 4.27 Nm (robot).
  • Showcased the potential for fusing additional sensor data to further enhance accuracy.

Conclusions:

  • The proposed method accurately estimates joint kinematics and kinetics from IMU data, overcoming common sensor limitations.
  • This approach holds promise for reliable motion analysis in clinical and home-based rehabilitation settings.
  • The algorithm's flexibility allows for fusion with other sensor types for improved performance.