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Related Concept Videos

Kinematic Equations - III01:18

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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,...
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Kinematic Equations - II01:17

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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...
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Kinematic Equations - I01:26

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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:
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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...
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Kinematic Equations for Rotation01:30

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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.
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Relative Motion Analysis using Rotating Axes01:25

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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.
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Related Experiment Video

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An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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JointTracker: Real-time inertial kinematic chain tracking with joint position estimation.

Bertram Taetz1,2, Michael Lorenz1, Markus Miezal1

  • 1Augmented Vision, German Research Center for Artificial Intelligence, Kaiserslautern, Rhineland-Palatinate, 67663, Germany.

Open Research Europe
|July 2, 2024
PubMed
Summary

This study introduces calibration-free inertial motion capture, enabling accurate online estimation of sensor poses and human skeleton joint positions without prior setup. This advances in-field motion analysis for diverse applications.

Keywords:
3D human pose estimation3D kinematicscalibration-freeinertial motion captureonline joint position estimationparameter estimationrecursive state estimation

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Area of Science:

  • Biomechanics
  • Robotics
  • Sensor Technology

Background:

  • In-field human motion capture (HMC) is gaining traction, with camera-based markerless systems facing limitations like restricted fields of view and occlusions.
  • Inertial motion capture offers an occlusion-free alternative for motion tracking outside laboratory settings, but requires spatial registration (sensor calibration).

Purpose of the Study:

  • To develop a calibration-free approach for inertial motion capture.
  • To enable simultaneous online estimation of sensor poses and joint positions for kinematic chains, such as the human skeleton.

Main Methods:

  • Proposes a recursive estimator for simultaneous online estimation of sensor poses and joint positions.
  • Derivation from an optimization objective is provided.
  • Applicable to synchronized data streams from body-mounted inertial sensor networks.

Main Results:

  • Demonstrates successful evaluations on noisy simulated data from a three-link chain.
  • Validated on real lower-body walking data from 25 healthy individuals.
  • Tested with walking data from a humanoid robot.

Conclusions:

  • The proposed method enables calibration-free inertial motion capture.
  • Facilitates accurate online estimation of human skeleton kinematics.
  • Offers a robust solution for in-field motion analysis and human-robot interaction.