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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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Force Classification01:22

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Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
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In mechanical engineering, a three-dimensional force system is a system of forces acting in three dimensions, with forces applied along the x, y, and z coordinate axes. The three-dimensional force system is an important concept in mechanical engineering, as it allows engineers to understand and analyze the behavior of objects and structures in three dimensions. By understanding the forces acting on a system, engineers can design more efficient and effective mechanical systems that can withstand...
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Three-Dimensional Force System:Problem Solving01:30

Three-Dimensional Force System:Problem Solving

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A three-dimensional force system refers to a scenario in which three forces act simultaneously in three different directions. This type of problem is commonly encountered in physics and engineering, where it is necessary to calculate the resultant force on the system, which can then be used to predict or analyze the behavior of the object or structure under consideration.
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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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Related Experiment Video

Updated: Jul 5, 2025

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography
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Glenohumeral joint force prediction with deep learning.

Pezhman Eghbali1, Fabio Becce2, Patrick Goetti3

  • 1Laboratory of Biomechanical Orthopedics, Ecole Polytechnique Fédérale de Lausanne, Institute of Bioengineering, Switzerland.

Journal of Biomechanics
|January 16, 2024
PubMed
Summary

Deep learning models accurately predict glenohumeral joint forces for total shoulder arthroplasty, significantly reducing computational costs compared to traditional musculoskeletal models.

Keywords:
Deep learningGlenohumeral joint forceMusculoskeletal model

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Area of Science:

  • Biomechanics
  • Computational modeling
  • Orthopedic surgery

Background:

  • Musculoskeletal models (MSM) require significant computational resources for joint and muscle force analysis.
  • Glenohumeral joint force is a critical outcome in total shoulder arthroplasty (TSA) influencing function, stability, and implant longevity.

Purpose of the Study:

  • To utilize deep learning models (DLM) for predicting glenohumeral joint force magnitude and direction.
  • To reduce the computational expense associated with MSM in TSA research.

Main Methods:

  • Generated 959 virtual subjects with patient-specific parameters using Markov-Chain Monte-Carlo simulation.
  • Developed and trained a DLM to predict glenohumeral joint force components in the scapula coordinate system.

Main Results:

  • DLM achieved high accuracy in predicting glenohumeral joint force components (R² of 0.97-0.98).
  • Mean absolute errors were approximately 2% of the maximum force, demonstrating reliability.
  • Significant reduction in computational costs compared to traditional MSM.

Conclusions:

  • DLM offers a reliable and computationally efficient alternative for estimating glenohumeral joint forces.
  • This approach can accelerate research and improve outcomes in total shoulder arthroplasty.