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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

Structural Classification of Joints

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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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Method of Joints: Problem Solving II01:30

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Consider a truss structure with frictionless joints fixed to a wall and roller support. If a force of 150 N is applied to joint A, the forces in each member of the truss can be determined using the method of joints.
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Muscle Coordination and Action01:24

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Muscle coordination is a complex and finely tuned process essential for smooth and purposeful movements like flexion, extension, adduction, abduction, and rotation. The human body orchestrates the actions of various muscles working in concert, each with a specific role. Four functional types describe how muscles work together: agonist, antagonist, synergist, and fixator.
Agonists
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Movement Joints in Buildings01:27

Movement Joints in Buildings

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Movement joints in buildings are essential design elements that accommodate inevitable motions caused by various factors such as temperature changes, moisture content variations, and structural deflections. These motions, if not considered in design and construction, can lead to unsightly or dangerous damage. Movement joints are incorporated in different forms to manage these stresses and allow materials to move without causing distress.
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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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Related Experiment Video

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Efficiently Recording the Eye-Hand Coordination to Incoordination Spectrum
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A guide to inter-joint coordination characterization for discrete movements: a comparative study.

Océane Dubois1, Agnès Roby-Brami2, Ross Parry3

  • 1Institute of Intelligent Systems and Robotics (CNRS-UMR 7222), University Pierre & Marie Curie, Paris, France. dubois@isir.upmc.fr.

Journal of Neuroengineering and Rehabilitation
|September 30, 2023
PubMed
Summary
This summary is machine-generated.

Choosing the right human movement analysis metric is key. This study found no single metric is ideal for characterizing inter-joint coordination, emphasizing a methodical selection process.

Keywords:
Discrete tasksInter-joint coordinationKinematic measurementMultiple jointsUpper-limb coordination

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Area of Science:

  • Biomechanics and Motor Control
  • Human Movement Analysis
  • Rehabilitation Engineering

Background:

  • Accurate human movement characterization is vital for understanding disorders and rehabilitation.
  • Advancements in motion capture have improved data acquisition.
  • Existing methods for inter-joint coordination lack consensus for discrete movement analysis.

Purpose of the Study:

  • To evaluate 12 inter-joint coordination metrics using a simulated dataset.
  • To assess metric performance against criteria derived from motor control physiology.
  • To provide guidance on selecting appropriate metrics for kinematic data analysis.

Main Methods:

  • Reviewed 12 inter-joint coordination metrics from existing literature.
  • Applied metrics to a simulated two-degree-of-freedom reaching movement dataset.
  • Evaluated each metric based on eight criteria related to human motor control.

Main Results:

  • No single inter-joint coordination metric proved universally ideal.
  • Metric suitability depends on specific movement characteristics and analysis goals.
  • Four key factors for metric selection were identified: temporal/spatial emphasis, explainability, dataset size, and computational resources.

Conclusions:

  • Selecting appropriate inter-joint coordination metrics is a significant scientific challenge.
  • A methodical approach considering specific analytical needs is crucial for effective human movement analysis.
  • Further research with larger, more variable datasets is recommended.