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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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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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One-Degree-of-Freedom System01:24

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In mechanical engineering, one-degree-of-freedom systems form the basis of a wide range of electrical and mechanical components. Using these models, engineers can predict the behavior of various parts in a larger system, which gives them insight into how different forces interact with each other.
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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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Understanding the movement of a rigid body in planar motion involves recognizing that every particle within this body is traversing a path that maintains a consistent distance from a specific plane. This concept is fundamental in the study of physics and mechanical engineering, and it allows us to comprehend better how objects move in space.
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Motion Recognition Based on Deep Learning and Human Joint Points.

Junping Wang1

  • 1Foundation Department, Huaibei Vocational and Technical College, Huaibei 23500, China.

Computational Intelligence and Neuroscience
|May 20, 2022
PubMed
Summary
This summary is machine-generated.

Deep learning improves human action recognition accuracy by analyzing joint movements. This method enhances motion analysis in sports and fitness applications, offering better insights into exercise form and performance.

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Area of Science:

  • Computer Science
  • Biomechanical Engineering

Background:

  • Traditional feature extraction for human action recognition relies on manual design, which is time-consuming and prone to errors.
  • Deep learning offers automated feature extraction, potentially improving accuracy and efficiency.

Purpose of the Study:

  • To develop a multiscene action similarity analysis algorithm using human joint points.
  • To improve the accuracy of human motion recognition using deep learning techniques.

Main Methods:

  • Implemented a deep learning approach based on convolutional neural networks for feature extraction.
  • Utilized Dynamic Time Warping (DTW) algorithm for analyzing joint angle sequences in sports scenes.
  • Applied cosine similarity to analyze joint angles for recognizing key fitness postures.

Main Results:

  • Achieved an improved motion recognition accuracy of 97.1%, a 0.19% increase over the previous method.
  • Identified specific joint movements (e.g., right knee) with significant DTW distances.
  • Demonstrated the algorithm's validity in analyzing action similarity across different scenes (fitness, sports).

Conclusions:

  • The deep learning-based approach significantly enhances human motion recognition accuracy.
  • The developed algorithm effectively analyzes action similarity using human joint points.
  • The findings have broad applications in sports recognition, training, and health management.