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Related Concept Videos

Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

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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.
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Relative Motion Analysis - Velocity01:24

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

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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.
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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...
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Related Experiment Video

Updated: May 3, 2026

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults
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Integrating OpenPose and SVM for Quantitative Postural Analysis in Young Adults: A Temporal-Spatial Approach.

Posen Lee1, Tai-Been Chen2, Hung-Yu Lin3

  • 1Department of Occupational Therapy, College of Medicine, I-Shou University, Kaohsiung 82445, Taiwan.

Bioengineering (Basel, Switzerland)
|June 27, 2024
PubMed
Summary

This study introduces a novel noninvasive method for postural analysis using OpenPose and Support Vector Machine (SVM) to analyze walking videos. The integrated approach achieves high accuracy in quantifying postural control without invasive sensors.

Keywords:
dynamic joint nodes plotpostural controlpostural quantificationtemporal and spatial regressionwalking pattern classification

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Last Updated: May 3, 2026

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Area of Science:

  • Computational Science
  • Biomechanical Analysis
  • Machine Learning Applications

Background:

  • Traditional posture monitoring often relies on invasive sensors, limiting real-time applications.
  • Significant potential exists to improve postural control quantification using readily available walking videos.

Purpose of the Study:

  • To develop and validate a noninvasive computational method for accurate postural analysis.
  • To integrate OpenPose deep learning with Support Vector Machine (SVM) for robust gait analysis.

Main Methods:

  • Utilized OpenPose for deep learning-based extraction of Dynamic Joint Nodes Plots (DJNP) and iso-block postural identity images.
  • Applied Temporal and Spatial Regression (TSR) models to extract key features for SVM classification.
  • Analyzed walking data from 35 young adults in controlled experiments.

Main Results:

  • Achieved an overall classification accuracy of 0.990 and a Kappa index of 0.985.
  • Demonstrated effective differentiation of left and right skews using the ratio of top angles (TAR) and ratio of bottom angles (BAR) with high AUC values.
  • Validated the efficacy of the integrated OpenPose-SVM approach for precise, real-time postural analysis.

Conclusions:

  • The integration of OpenPose and SVM offers a highly accurate and robust noninvasive solution for postural control quantification.
  • This method shows promise for revolutionizing clinical applications in rehabilitation and sports science.
  • Future research will explore broader demographics and advanced machine learning models for enhanced adaptability.