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Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

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

Relative Motion Analysis using Rotating Axes

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 instrumental in...
Relative Motion Analysis - Acceleration01:10

Relative Motion Analysis - Acceleration

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

Relative Motion Analysis using Rotating Axes - Acceleration

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...
Relative Motion Analysis - Velocity01:24

Relative Motion Analysis - Velocity

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...
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

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...

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Related Experiment Video

Updated: May 14, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Multi-label classification for the analysis of human motion quality.

Portia E Taylor1, Gustavo J M Almeida, Jessica K Hodgins

  • 1Biomedical Engineering Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA. pet@cs.cmu.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|February 1, 2013
PubMed
Summary

This study developed a wearable sensor system to assess human motion quality for home rehabilitation. It accurately detects incorrect exercises, paving the way for personalized feedback and improved patient motivation.

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Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
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Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

Related Experiment Videos

Last Updated: May 14, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable
09:24

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

Area of Science:

  • Biomedical Engineering
  • Rehabilitation Technology
  • Wearable Sensors

Background:

  • Accurate assessment of exercise performance is crucial for effective home rehabilitation.
  • Identifying specific motion errors, not just correctness, is key for providing targeted user feedback.
  • Knee osteoarthritis (OA) presents challenges for rehabilitation due to pain and mobility limitations.

Purpose of the Study:

  • To develop and evaluate methods for assessing human motion quality using body-worn inertial sensors.
  • To utilize multi-label classifiers for detecting subtle errors in exercise performances.
  • To lay the groundwork for an at-home rehabilitation device offering error recognition and feedback.

Main Methods:

  • Employing tri-axial accelerometers and gyroscopes for motion data acquisition.
  • Implementing multi-label classification with decision tree-based algorithms.
  • Testing the system on eight individuals with knee osteoarthritis performing exercises.

Main Results:

  • The multi-label classifier achieved 75% sensitivity, 90% specificity, and 80% accuracy in detecting exercise errors.
  • Demonstrated the feasibility of using wearable sensors for detailed motion analysis in a clinical population.
  • Validated the effectiveness of machine learning for identifying specific exercise deviations.

Conclusions:

  • Wearable sensor technology combined with machine learning offers a viable solution for objective motion quality assessment in rehabilitation.
  • The developed methods can form the basis of intelligent at-home rehabilitation systems.
  • Such systems have the potential to enhance patient adherence and therapeutic outcomes by providing real-time, specific feedback.