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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
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Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
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Related Experiment Video

Updated: Jul 23, 2025

Evaluating Postural Control and Lower-extremity Muscle Activation in Individuals with Chronic Ankle Instability
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Core stability status classification based on mediolateral head motion during rhythmic movements and functional

Siwoo Jeong1, Si-Hyun Kim2, Kyue-Nam Park1

  • 1Department of Physical Therapy, Jeonju University, Jeonju, Korea.

Digital Health
|July 12, 2023
PubMed
Summary

This study developed a simple model using head motion analysis during rhythmic movements or functional tests to accurately assess core stability. The findings aid in preventing low back pain by identifying individuals with poor core stability.

Keywords:
Core stabilitycyclingfunctional movement testsgaitmachine learningmediolateral head motion

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Area of Science:

  • Biomechanics
  • Sports Medicine
  • Rehabilitation

Background:

  • Core stability is crucial for preventing low back pain.
  • Poor core stability is a significant risk factor for low back pain.
  • Automated assessment methods are needed for efficient evaluation.

Purpose of the Study:

  • To develop a simple model for automated core stability assessment.
  • To evaluate the effectiveness of head motion analysis for core stability classification.
  • To identify key features for accurate core stability status determination.

Main Methods:

  • An inertial measurement unit sensor in a wireless earbud estimated mediolateral head angle during rhythmic movements (cycling, walking, running) and functional movement tests (single-leg squat, lunge, side lunge).
  • Support vector machine and neural network models were trained using head angle data features like symmetry index and amplitude.
  • Participants (n=77) were classified into good and poor core stability groups based on established tests.

Main Results:

  • Support vector machine models achieved approximately 87% accuracy in classifying core stability.
  • Neural network models achieved approximately 75% accuracy.
  • Accuracy was consistent across feature sets derived from rhythmic movements, functional movement tests, or a combination.

Conclusions:

  • An automated model using head motion features from rhythmic movements or functional tests can accurately classify core stability status.
  • This technology offers a practical approach for assessing core stability and potentially preventing low back pain.
  • The developed model provides a foundation for further research in automated biomechanical assessments.