Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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 immobile...

Structural Classification of Joints

Structural Classification of Joints

Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Gene expression based leukemia sub-classification using committee neural networks.

Bioinformatics and biology insights·2010

Same author

Facial expression (mood) recognition from facial images using committee neural networks.

Biomedical engineering online·2009

Same author

Toward a fuzzy logic control of the infant incubator.

Annals of biomedical engineering·2009

Same author

Toward direct biocontrol using surface EMG signals: control of finger and wrist joint models.

Medical engineering & physics·2006

Same author

Fractal analysis of acceleration signals from patients with CPPD, rheumatoid arthritis, and spondyloarthroparthy of the finger joint.

Computer methods and programs in biomedicine·2005

Same author

Speaker verification using committee neural networks.

Computer methods and programs in biomedicine·2003

Search research articles

Related Experiment Video

Updated: Jun 26, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

Neural network committees for finger joint angle estimation from surface EMG signals.

Nikhil A Shrirao¹, Narender P Reddy, Durga R Kosuri

¹Department of Biomedical Engineering, University of Akron, Akron, OH 44325-0302, USA. nikhil@terarecon.com

Biomedical Engineering Online

|January 22, 2009

Summary

Researchers developed a neural network model to predict finger joint angles from surface electromyography (SEMG) signals, enabling more intuitive control for virtual reality (VR) systems.

More Related Videos

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Related Experiment Videos

Last Updated: Jun 26, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study

Published on: November 6, 2015

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Area of Science:

Biomedical Engineering
Neuroscience
Virtual Reality

Background:

Virtual reality (VR) systems typically use hand and finger positioning for control.
Surface electromyography (SEMG) offers a potential for more intuitive and unconstraining biocontrol of VR environments.

Purpose of the Study:

To develop a technique for predicting finger joint angles from SEMG signals using neural networks.
To enable direct biocontrol of VR environments through muscle electrical activity.

Main Methods:

Collected SEMG and actual finger joint angle data during index finger flexion-extension at varying speeds.
Trained multiple neural networks to predict joint angles from SEMG parameters.
Formed committees of the best-performing neural networks for evaluation.

Main Results:

Neural network committees accurately predicted finger joint angles despite hysteresis in SEMG signals.
Root-mean-square errors for prediction ranged from 0.085 to 0.163 degrees.
Prediction accuracy was consistent across different speeds of finger movement.

Conclusions:

Neural network committees effectively predict finger joint angles from SEMG signals.
This technique holds promise for enhancing user interaction and control in virtual reality.