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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
An...
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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.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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Updated: Nov 18, 2025

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
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Semi-Supervised Joint Learning for Hand Gesture Recognition from a Single Color Image.

Chi Xu1,2,3, Yunkai Jiang1,2, Jun Zhou1,2

  • 1School of Automation, China University of Geosciences, Wuhan 430074, China.

Sensors (Basel, Switzerland)
|February 5, 2021
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Summary
This summary is machine-generated.

This study introduces a deep learning method for simultaneous hand gesture recognition and 3D hand pose estimation. Jointly learning features boosts gesture recognition accuracy, even with limited annotated data.

Keywords:
hand gesture recognitionhand pose estimationjoint learningshared feature

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Hand gesture recognition and hand pose estimation are related tasks.
  • Existing methods may lack robustness in unconstrained environments.
  • Data annotation for these tasks can be challenging.

Purpose of the Study:

  • To develop a deep learning approach for joint hand gesture recognition and 3D hand pose estimation.
  • To improve gesture recognition accuracy by leveraging shared features.
  • To address data scarcity using a semi-supervised training scheme.

Main Methods:

  • A deep learning model that jointly learns shared features for both tasks.
  • A semi-supervised training strategy to handle limited annotations.
  • Simultaneous foreground hand detection, gesture recognition, and 3D pose estimation.

Main Results:

  • The proposed approach achieves significantly improved gesture recognition accuracy.
  • Leveraging shared features from hand pose estimation enhances gesture recognition.
  • A new dataset for evaluating hand gesture recognition in unconstrained settings was introduced.

Conclusions:

  • Jointly learning features for correlated tasks is an effective strategy.
  • Semi-supervised learning is beneficial for training models with limited data.
  • The proposed method offers a robust solution for hand gesture recognition and pose estimation.