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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

5.7K
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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Knee Joint01:23

Knee Joint

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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...
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Ankle Joint01:10

Ankle Joint

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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...
2.2K
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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Development of the Limb Synovial Joints01:07

Development of the Limb Synovial Joints

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Joints form during embryonic development in conjunction with the formation and growth of the associated bones. The embryonic tissue that gives rise to all bones, cartilage, and connective tissues of the body is called mesenchyme.
The mesenchymal stem cells differentiate into chondrocytes that form the hyaline cartilage, and later the cartilaginous model of the bone. This model further transforms into a bone. This process is known as endochondral ossification.
During development, the limbs...
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Related Experiment Video

Updated: Nov 3, 2025

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
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EMG and Joint Angle-Based Machine Learning to Predict Future Joint Angles at the Knee.

Jordan Coker1, Howard Chen1, Mark C Schall2

  • 1Department of Mechanical Engineering, Auburn University, Auburn, AL 36849, USA.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

Predicting knee flexion angles using electromyography (EMG) signals with artificial neural networks (ANN) is feasible. Accuracy decreases with longer prediction times, but improves with more training data.

Keywords:
EMGjoint anglemachine learningprediction

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Area of Science:

  • Biomechanics
  • Neuroscience
  • Machine Learning

Background:

  • Electromyography (EMG) measures skeletal muscle electrical activity.
  • Advancing exoskeleton technology necessitates predicting human intent for control.
  • EMG signals show potential for real-time control applications.

Purpose of the Study:

  • To predict knee flexion angles during gait using EMG signals and an artificial neural network (ANN).
  • To evaluate the impact of prediction time into the future on accuracy.
  • To assess the effect of training data volume on ANN performance.

Main Methods:

  • An ANN was trained and tested using knee flexion angles and EMG signals.
  • Predictions were made for gait at 50, 100, 150, and 200 ms into the future.
  • Root mean square error (RMSE) was used to quantify prediction accuracy.

Main Results:

  • Prediction accuracy was significantly affected by time into the future (p < 0.001).
  • Increased prediction time led to decreased accuracy, with RMSE increasing from 0.68 to 4.62 degrees.
  • Higher numbers of training trials improved ANN prediction accuracy.

Conclusions:

  • Time into the future is the primary factor influencing knee flexion angle prediction accuracy.
  • Increased training data enhances the predictive performance of ANNs for gait analysis.
  • EMG-based intent prediction holds promise for advanced exoskeleton control systems.