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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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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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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Related Experiment Video

Updated: May 28, 2025

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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Exploring Trends and Clusters in Human Posture Recognition Research: An Analysis Using CiteSpace.

Lichuan Yan1, You Du1

  • 1College of Furnishings and Industrial Design, Nanjing Forestry University, Nanjing 210037, China.

Sensors (Basel, Switzerland)
|February 13, 2025
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Summary

This study maps human posture recognition research, highlighting deep learning and sensor methods. The field is maturing, with future innovation likely in interdisciplinary biomechanics and engineering integrations.

Keywords:
CiteSpacehuman activity recognition (HAR)human posture recognitioninertial measurement unit (IMU)knowledge graphvisual analysis

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Area of Science:

  • Computer Science
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Human posture recognition is crucial for applications in healthcare, sports, and human-computer interaction.
  • Existing research spans vision-based and non-vision-based methodologies.
  • The field has seen significant advancements driven by machine learning and sensor technologies.

Purpose of the Study:

  • To systematically analyze interdisciplinary research directions in human posture recognition.
  • To identify key trends, knowledge structures, and innovation pathways.
  • To provide a roadmap for future research and development.

Main Methods:

  • Bibliometric analysis of 3066 research papers (2011-2024) using CiteSpace software.
  • In-depth citation analysis to identify research clusters and key documents.
  • Analysis of publication growth trends and interdisciplinary indicators (e.g., citation bursts, centrality).

Main Results:

  • Five significant research clusters were identified, with deep learning and sensor-based methods dominating recent advancements.
  • Deep learning models achieved high accuracy (e.g., 99.7%) with minimal delay.
  • The field shows signs of maturation, with a predicted slowdown in major breakthroughs.
  • A clear trend towards interdisciplinary approaches, integrating biomechanics with engineering, is emerging.

Conclusions:

  • Human posture recognition is a maturing field with current dominance by deep learning and sensor technologies.
  • Future innovation lies in the convergence of biomechanics, engineering, and advanced computational methods.
  • The study provides a comprehensive overview to guide future research and identify innovation gaps.