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Related Concept Videos

Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

492
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...
492
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

397
A stroke engine has a slider-crank mechanism that converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider.
When an external force is exerted, it sets the crank into a rotational movement. This, in turn, instigates the motion of the connecting rod, leading to what is referred to as a general plane motion. This process involves two key points - point A on the connecting rod...
397
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

245
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...
245
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

386
A slider-crank mechanism converts rotational motion from the crank into linear motion of the slider or vice versa. This mechanism consists of three main parts: the crank, the connecting rod, and the slider. The movement of the slider-crank is an example of general plane motion as the fluctuating angle between the crank and the connecting rod. Consider a segment AB where point A is at the end of the slider and point B is on the diametrically opposite end to point A, on a crack. The variance in...
386
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

428
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...
428
Relative Motion Analysis using Rotating Axes - Acceleration01:22

Relative Motion Analysis using Rotating Axes - Acceleration

361
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...
361

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Related Experiment Video

Updated: Jul 30, 2025

Mouse Short- and Long-term Locomotor Activity Analyzed by Video Tracking Software
10:15

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Published on: June 20, 2013

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Markerless Motion Tracking With Noisy Video and IMU Data.

Soyong Shin, Zhixiong Li, Eni Halilaj

    IEEE Transactions on Bio-Medical Engineering
    |May 12, 2023
    PubMed
    Summary

    Deep learning models using video and inertial sensors estimate human movement accurately without calibration. These tools democratize research-grade motion analysis for clinical use.

    Area of Science:

    • Biomechanics
    • Medical Technology
    • Artificial Intelligence

    Background:

    • Marker-based motion capture is the gold standard for human movement analysis but is costly and requires expertise.
    • Clinical adoption is hindered by the need for precise sensor calibration, which is time-consuming and error-prone.
    • Advances in inertial sensing and computer vision offer potential for accessible, research-grade motion analysis.

    Purpose of the Study:

    • To develop deep learning models for human movement estimation using video and inertial sensor data.
    • To eliminate the need for meticulous sensor-to-body calibration in motion analysis.
    • To create tools for accurate movement assessment in clinical and natural settings.

    Main Methods:

    • Proposed deep learning models: VideoNet (video), IMUNet (inertial sensors), and FusionNet (combined).

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  • Models trained on synthetically generated data from marker-based motion capture, augmented for sensor misplacement and camera occlusion.
  • Tested models with real-world data including walking, jogging, squatting, and sit-to-stand movements.

    • Main Results:

    • IMUNet achieved state-of-the-art accuracy on calibrated data.
    • VideoNet and FusionNet reduced root-mean-squared errors by 7.6 ± 5.4° and 5.9 ± 3.3°, respectively.
    • Models demonstrated reduced sensitivity to noise, decreasing errors significantly with sensor misplacement and joint-center estimation inaccuracies.

    Conclusions:

    • Developed deep learning tools enable research-grade movement estimation without time-consuming calibration.
    • These models reduce costs associated with commercial motion capture systems.
    • The technology aims to democratize diagnosis, prognosis, and treatment of neuromusculoskeletal conditions.