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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

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 immobile...
Knee Joint01:23

Knee Joint

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 group...

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Related Experiment Video

Updated: Jun 21, 2026

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty
07:33

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty

Published on: May 5, 2023

A neural network for predicting knee contact forces from clinic-friendly data.

Yumei Sun1, Claudio Pizzolato1, Laura E Diamond1

  • 1Australian Centre for Precision Health and Technology (PRECISE), Griffith University, Gold Coast, Australia; School of School of Allied Health, Sport and Social Work, Griffith University, Gold Coast, Australia.

Journal of Biomechanics
|June 19, 2026
PubMed
Summary

Artificial intelligence (AI) models can accurately estimate lower limb joint biomechanics and knee contact forces using minimal clinic-friendly data. This approach simplifies complex analyses, paving the way for wider clinical adoption of AI in biomechanics.

Keywords:
Deep neural networkJoint kinematicsMuscle forcesNeuromusculoskeletal modellingOsteoarthritis

Related Experiment Videos

Last Updated: Jun 21, 2026

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty
07:33

In Vitro Application of a Wireless Sensor in Flexion-Extension Gap Balance of Unicompartmental Knee Arthroplasty

Published on: May 5, 2023

Area of Science:

  • Biomechanics
  • Artificial Intelligence
  • Musculoskeletal Modeling

Background:

  • Physics-based neuromusculoskeletal models are valuable for biomechanical analysis but require extensive data and expertise.
  • Artificial intelligence (AI) offers a promising alternative by learning complex relationships from data.
  • AI enables scalable estimation of internal biomechanics using readily available clinical data.

Purpose of the Study:

  • To develop and evaluate a bi-directional long short-term memory (BiLSTM) model for estimating lower limb joint kinematics, moments, and knee contact force.
  • To assess the influence of surface electromyography (EMG) number and combinations on prediction accuracy.
  • To identify an optimal EMG configuration for accurate and practical biomechanical analysis.

Main Methods:

  • A BiLSTM model was developed to predict lower limb joint kinematics, moments, and knee contact force.
  • Clinic-friendly data including height, weight, sex, key point positions, and surface EMG were used as inputs.
  • The study explored various EMG numbers and combinations to determine their impact on prediction accuracy.

Main Results:

  • The BiLSTM model achieved high accuracy in predicting kinematics (R²=0.94) and moments (R²=0.91).
  • Knee contact force prediction was also accurate (R²=0.81) using an optimal 3-EMG configuration.
  • Vastus medialis, gastrocnemius medialis, and gastrocnemius lateralis muscles were key contributors to accurate knee contact force prediction.

Conclusions:

  • Minimal-EMG AI models, like the BiLSTM, can accurately predict complex biomechanical parameters.
  • The findings suggest a practical and accurate approach for AI-based biomechanical analysis in clinical settings.
  • Reduced data requirements and complexity can facilitate the adoption of AI in clinical biomechanics.