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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 15, 2025

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

Predicting Severe Knee Arthritis Based on Two Inertial Measurement Unit Sensors as a Dynamic Coordinate System Using

Erfan Azizi¹, Mohammadsadegh Darbankhalesi¹, Amirhossein Zare²

¹Department of Bioelectrics and Biomedical Engineering, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.

Journal of Medical Signals and Sensors

|April 7, 2025

Summary

This summary is machine-generated.

A wearable device using inertial measurement unit (IMU) sensors can accurately differentiate between healthy individuals and those with severe knee osteoarthritis (KOA). This non-invasive method shows promise for diagnosing KOA, offering an alternative to traditional X-rays.

Keywords:

Classification dynamic coordinates feature extraction inertial measurement unit osteoarthritis

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Area of Science:

Biomedical Engineering
Orthopedics
Wearable Technology

Background:

Musculoskeletal disorders, particularly knee osteoarthritis (KOA), pose a significant healthcare challenge due to aging populations.
Current KOA diagnosis relies on radiographs, which involve ionizing radiation and have drawbacks.
There is a need for non-invasive, low-cost diagnostic methods for KOA.

Purpose of the Study:

To evaluate the efficacy of a wearable device in distinguishing between healthy individuals and those with severe KOA (grade 4).
To explore the potential of wearable sensor data for KOA diagnosis.

Main Methods:

A wearable device with two inertial measurement unit (IMU) sensors (thigh and lower leg) was utilized.
New features were extracted in a dynamic coordinate system from 1433 IMU signals from 15 healthy and 15 severe KOA individuals (aged >45).
Four classifiers (naive Bayes, KNN, SVM, random forest) were evaluated using 10-fold cross-validation.

Main Results:

The K-nearest neighbors (KNN) classifier achieved the highest accuracy at 93.71 ± 1.1%.
KNN also demonstrated high precision at 93 ± 1.31%.
The proposed algorithm showed improved sensitivity compared to existing methods.

Conclusions:

Novel features derived from a dynamic coordinate system and the KNN model effectively diagnose between healthy individuals and KOA patients.
The wearable device shows potential as an auxiliary tool for arthritis diagnosis.
Further validation is needed, as results are specific to severe KOA (grade 4) and may differ for other grades.