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Related Concept Videos

Knee Joint01:23

Knee Joint

2.9K
The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris...
2.9K
Functional Classification of Joints01:09

Functional Classification of Joints

6.1K
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...
6.1K
Structural Classification of Joints01:20

Structural Classification of Joints

6.4K
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...
6.4K
Ankle Joint01:10

Ankle Joint

2.4K
The ankle is formed by the talocrural joint (crural = leg). It consists of the articulations between the talus bone of the foot and the distal ends of the tibia and fibula of the leg. The superior aspect of the talus bone is square-shaped and has three areas of articulation. The top of the talus articulates with the inferior tibia. This is the portion of the ankle joint that carries the body weight between the leg and foot. The sides of the talus are firmly held in position by the articulations...
2.4K

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Related Experiment Video

Updated: Dec 6, 2025

In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy
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In Vivo Quantification of Hip Arthrokinematics during Dynamic Weight-bearing Activities using Dual Fluoroscopy

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Graphical-based multivariate analysis for knee joint clinical and kinematic data correlation assessment.

Fatima Bensalma, Glen Richardson, Youssef Ouakrim

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
    |October 6, 2020
    PubMed
    Summary

    This study reveals how biomechanical data and clinical symptoms correlate in knee osteoarthritis (OA) patients. Multivariate statistical methods help uncover these associations for better pathology assessment.

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    Area of Science:

    • Biomechanics
    • Orthopedics
    • Medical Statistics

    Background:

    • Clinical gait analysis relies on extensive joint kinematics, kinetics, and functional measurements for decision-making.
    • Understanding the link between biomechanical and clinical data is crucial for knee osteoarthritis (OA) patient management.

    Purpose of the Study:

    • To analyze and visualize the correlation structure between biomechanical characteristics and clinical symptoms in end-staged knee OA patients.
    • To provide knowledge from the coupling of these parameters for improved knee-joint disease pathology assessment.

    Main Methods:

    • Employed two multivariate statistical approaches: Canonical Correlation Analysis (CCA) and graphical-based representation.
    • Assessed multivariate association between biomechanical features and clinical symptoms.

    Main Results:

    • Demonstrated the utility of multivariate approaches in highlighting specific correlation structures.
    • Successfully extracted meaningful information from the coupling of biomechanical and clinical data.

    Conclusions:

    • Multivariate statistical methods are effective for understanding complex relationships in gait analysis data.
    • The findings offer valuable insights for assessing knee joint pathology in OA patients.