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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: May 4, 2026

Computerized Dynamic Posturography for Postural Control Assessment in Patients with Intermittent Claudication

Computerized Dynamic Posturography for Postural Control Assessment in Patients with Intermittent Claudication

Published on: December 11, 2013

Implementing Machine Learning Algorithms to Classify Postures and Forecast Motions When Using a Dynamic Chair.

Ghazal Farhani¹, Yue Zhou², Patrick Danielson³

¹Department of Electrical and Computer Engineering, Western University, London, ON N6A 3K7, Canada.

Sensors (Basel, Switzerland)

|January 11, 2022

Summary

This summary is machine-generated.

Machine learning accurately identifies sitting postures (90%+) and predicts future movements (97%). This technology can promote healthier sitting habits by increasing user awareness of their posture and suggesting alternatives.

Keywords:

1D-CNN-LSTM dynamic chairs long short-term memory (LSTM)machine learning application posture classification

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Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

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Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

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Published on: August 30, 2016

Area of Science:

Ergonomics
Computer Science
Health Informatics

Background:

Prolonged sitting in modern jobs poses health risks.
Traditional chairs offer limited ergonomic benefits.
Users often lack awareness of their posture and dynamic chair capabilities.

Purpose of the Study:

To develop a system for real-time posture identification and prediction.
To enhance user awareness of sitting behavior.
To leverage machine learning for ergonomic sitting solutions.

Main Methods:

Implemented machine learning algorithms: random forest, gradient decision tree, and support vector machine for posture classification.
Utilized a 1D-convolutional-LSTM network for forecasting future postures.
Trained and evaluated models on user sitting data.

Main Results:

Posture classification achieved over 90% accuracy.
Future posture prediction reached 97% accuracy.
The system provides accurate sitting habit reports.

Conclusions:

Machine learning effectively classifies and predicts sitting postures.
The developed system can promote healthier sitting habits through real-time feedback and predictive insights.
Future work can integrate posture suggestions based on predictions.