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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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Structural Classification of Joints01:20

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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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

Relative Motion Analysis using Rotating Axes-Problem Solving

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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.
Here, in order to determine the magnitude of velocity and acceleration for point...
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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 - Acceleration01:10

Relative Motion Analysis - Acceleration

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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

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Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
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Cross View Gait Recognition Using Joint-Direct Linear Discriminant Analysis.

Jose Portillo-Portillo1, Roberto Leyva2, Victor Sanchez3

  • 1Instituto Politécnico Nacional, ESIME Culhuacan, 04430 Coyoacán, CDMX, Mexico. jportillop1300@alumno.ipn.mx.

Sensors (Basel, Switzerland)
|December 28, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a novel view-invariant gait recognition system using Direct Linear Discriminant Analysis (DLDA) to improve accuracy and reduce computational load. The framework effectively handles varying angles and the under-sampling problem for robust human identification.

Keywords:
KNN classifierdirect linear discriminant analysis (DLDA)gait energy image (GEI)gait recognitionview-invariant methods

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Area of Science:

  • Computer Vision
  • Biometrics
  • Pattern Recognition

Background:

  • Gait recognition is a challenging biometric modality due to variations in viewing angles.
  • Existing methods often struggle with view invariance and the under-sampling problem (USP).

Purpose of the Study:

  • To propose a novel view-invariant gait recognition framework.
  • To enhance classification accuracy and reduce computational complexity.

Main Methods:

  • Utilized Direct Linear Discriminant Analysis (DLDA) for dimensionality reduction.
  • Developed a unique view-invariant model using Gait Energy Images (GEIs).
  • Created a single joint model for classifying GEIs from different angles.

Main Results:

  • The proposed framework demonstrated improved recognition accuracy compared to other view-invariant methods.
  • Achieved a reduction in computational complexity.
  • Effectively addressed the under-sampling problem (USP).

Conclusions:

  • The DLDA-based framework offers a robust solution for view-invariant gait recognition.
  • The method shows significant potential for practical biometric applications.