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Functional Classification of Joints

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...

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Related Experiment Video

Updated: Jan 1, 2026

Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation

Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation

Published on: December 13, 2024

Classifying neck pain status using scalar and functional biomechanical variables - development of a method using

Bernard X W Liew¹, David Rugamer², Almond Stocker³

¹School of Sport, Rehabilitation and Exercise Sciences, University of Essex, Colchester, Essex, CO4 3SQ, United Kingdom.

|December 20, 2019

Summary

Neck pain patients exhibit distinct movement patterns during walking. A statistical model accurately classified neck pain status using specific trunk and hip biomechanical variables, indicating a stiffer walking strategy in those with neck pain.

Keywords:

Biomechanics Functional regression Machine learning Neck pain Walking

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Published on: September 21, 2017

Area of Science:

Biomechanics
Clinical Biomechanics
Musculoskeletal Health

Background:

Individuals with neck pain display altered movement and muscle activation patterns compared to healthy individuals.
High dimensionality of biomechanical data presents challenges for prognostic modeling.

Purpose of the Study:

To evaluate the classification performance of a statistical model using both scalar and functional biomechanical covariates for neck pain status.
To identify key biomechanical predictors of neck pain.

Main Methods:

Motion capture and electromyography were used to assess 21 healthy and 26 neck pain individuals across three gait conditions.
Functional data boosting (FDboost) was employed to classify neck pain status using 94 selected biomechanical covariates after collinearity reduction.

Main Results:

The model achieved an Area Under the Curve (AUC) of 80.8% in classifying neck pain status.
Key predictors included trunk lateral flexion (CCW gait), trunk flexion (CW gait), and hip jerk index (CCW gait).
Specific trunk and hip movements were significantly associated with increased odds of having neck pain.

Conclusions:

Individuals with neck pain may adopt a stiffer walking strategy during curvilinear gait, increasing their risk.
Functional data boosting (FDboost) offers a viable method for creating interpretable models from complex, high-dimensional biomechanical data.
This approach holds potential for future prognostic modeling in neck pain research.