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Related Concept Videos

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.
A fibrous joint is where the adjacent bones are united by fibrous connective...
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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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Composite Bodies00:55

Composite Bodies

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A composite body is a body made up of multiple parts, connected to form a larger, unified object. Each part has its own weight and center of gravity, which must be considered to determine the center of gravity of the composite body. In cases where the density or specific weight is constant, the center of gravity coincides with the centroid.
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Method of Joints: Problem Solving II01:30

Method of Joints: Problem Solving II

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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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Virtual Work for a System of Connected Rigid Bodies01:06

Virtual Work for a System of Connected Rigid Bodies

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Virtual work is a powerful method used to solve problems involving several connected rigid bodies. When the system is in equilibrium, virtual work is zero. This allows the calculation of the resulting forces when a system undergoes a virtual displacement. When attempting to analyze such a system, first, use a free-body diagram, where an independent coordinate represents the configuration of the links, and mark its deflected position resulting from the positive virtual displacement.
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Method of Joints01:30

Method of Joints

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The method of joints is a commonly used technique to analyze the forces in structural trusses. The method is based on the principle of equilibrium, which assumes that the truss members are connected by frictionless pins. The forces at each joint can be determined by considering the equilibrium of the forces acting on that joint.
Since plane truss members are in the same plane, each joint is subjected to a coplanar and concurrent force system. To apply the method of joints, the first step is to...
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Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
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BPJDet: Extended Object Representation for Generic Body-Part Joint Detection.

Huayi Zhou, Fei Jiang, Jiaxin Si

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 16, 2024
    PubMed
    Summary

    This study introduces a novel Body-Part Joint Detector (BPJDet) for unified human body and part detection. BPJDet improves association accuracy and generalizes to various datasets and animals, enhancing downstream applications.

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    Area of Science:

    • Computer Vision
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Existing Convolutional Neural Networks (CNNs)-based human body and part detection methods often train independently, hindering accurate part-to-body association.
    • The challenge lies in effectively linking detected body parts to the overall human form within complex scenes.

    Purpose of the Study:

    • To develop a unified framework for the joint detection of human bodies and their constituent parts.
    • To introduce a novel object representation that integrates body parts for improved association.
    • To create an end-to-end generic detector, the Body-Part Joint Detector (BPJDet), capable of synergistic multi-task optimization.

    Main Methods:

    • Proposed a novel extended object representation incorporating center-offsets of body parts.
    • Constructed an end-to-end generic Body-Part Joint Detector (BPJDet).
    • Utilized a unified representation embedding body-part associations with semantic and geometric information, suitable for both anchor-based and anchor-free detectors.

    Main Results:

    • BPJDet demonstrated state-of-the-art association performance across multiple body-part and body-parts datasets (CityPersons, CrowdHuman, BodyHands, COCOHumanParts, Animals5C).
    • Achieved high detection accuracy while maintaining a favorable speed-accuracy trade-off, avoiding error-prone post-matching.
    • Showcased generalization capabilities for detecting parts of humans and quadruped animals.

    Conclusions:

    • The proposed BPJDet effectively addresses the challenge of joint human body and part detection through an integrated representation.
    • BPJDet offers superior body-part association accuracy and versatility, outperforming existing methods.
    • The enhanced association capability significantly improves performance in downstream applications like crowd head detection and hand contact estimation.