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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
An...
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Related Experiment Video

Updated: Jan 15, 2026

Evaluating Postural Control and Lower-extremity Muscle Activation in Individuals with Chronic Ankle Instability
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A Preliminary Data-Driven Approach for Classifying Knee Instability During Subject-Specific Exercise-Based Game with

Priyanka Ramasamy1, Poongavanam Palani2, Gunarajulu Renganathan3

  • 1Graduate School of Advanced Science and Engineering, Hiroshima University, Hiroshima 739-8527, Japan.

Sensors (Basel, Switzerland)
|October 16, 2025
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Summary
This summary is machine-generated.

This study developed a gaming system to track knee instability during squats, achieving 96% accuracy in detecting issues. The system uses multiple sensors to enhance lower limb training safety and effectiveness.

Keywords:
exergamesknee instabilitylong short-term memorysquatssupport vector machine

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Area of Science:

  • Biomechanics
  • Rehabilitation Engineering
  • Machine Learning

Background:

  • Lower limb functional degeneration is increasing, impacting core strength and motor control.
  • Improper squat techniques can cause dynamic knee instability, reducing motivation for training.
  • Exergame systems are needed to improve user experience and prevent injuries during lower limb training.

Purpose of the Study:

  • To develop and validate a gaming-based exercise tracking system for real-time detection of knee instability during squats.
  • To assess the effectiveness of multimodal sensor fusion for improving the accuracy of knee instability classification.
  • To investigate the performance of Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) models in identifying knee instability events.

Main Methods:

  • 28 healthy subjects participated in exergame-based squat training.
  • Dynamic kinematic features were collected using a depth camera-based inertial measurement unit (IMU) and an Anima forceplate sensor.
  • Spearman correlation was used for feature selection, and LSTM and SVM models were trained for binary classification of knee instability.

Main Results:

  • Knee instability events were successfully classified with high accuracy (96%) using both LSTM and SVM models.
  • Feature selection identified key indicators of knee instability, including knee shakiness, knee distance, squat depth, sway velocity, and sway area.
  • The multimodal sensor approach significantly improved classifier performance compared to single-modality methods.

Conclusions:

  • The proposed system effectively tracks knee instability in real-time using multimodal sensor data and machine learning.
  • This approach enhances the safety and efficacy of lower limb training, particularly in gamified rehabilitation settings.
  • The findings support the use of integrated sensor systems for personalized and adaptive physical therapy.