Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Seizures: Classification01:13

Seizures: Classification

558
Epilepsy is primarily characterized by unpredictable seizures, either provoked by an identifiable factor, such as injury or illness, or unprovoked, occurring spontaneously without apparent cause.
Seizures are typically classified into two main categories: focal and generalized seizures.
Focal Seizures
Focal seizures originate from specific regions of the brain. These seizures are further sub-classified into two types:
558
Prosopagnosia01:24

Prosopagnosia

237
Prosopagnosia, also known as face blindness, is the inability to recognize faces. In severe cases, individuals with prosopagnosia may not recognize close family members, including parents and spouses, by their faces. For instance, someone with prosopagnosia might walk past their child in a crowd, only realizing their mistake upon noticing their child's distinctive backpack or favorite jacket. Prosopagnosia specifically impairs facial recognition, while the recognition of other objects or...
237
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

56.8K
Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
56.8K
Classification of Skeletal Muscle Relaxants01:28

Classification of Skeletal Muscle Relaxants

2.6K
Skeletal muscle relaxants are a group of drugs that can reduce muscle stiffness and induce temporary paralysis to relieve pain. These agents can act centrally to reduce muscle tone or spasms in painful conditions such as multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), or spinal injuries; they are called antispasmodics or spasmolytics.
Peripherally acting skeletal muscle relaxants interfere with the neurotransmission at the neuromuscular end plate to induce paralysis during...
2.6K
Force Classification01:22

Force Classification

1.4K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.4K
Classification of Illness01:17

Classification of Illness

7.8K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
7.8K

You might also read

Related Articles

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

Sort by
Same author

Tardive Dystonia Described as Tardive Tremor.

Journal of general and family medicine·2026
Same author

Assessment of serum FAM19A5 level in patients with Neuromyelitis Optica spectrum disorder.

Multiple sclerosis and related disorders·2026
Same author

Development and expert refinement of a stratified framework for progression independent of relapse activity (PIRA) in multiple sclerosis.

Clinical neurology and neurosurgery·2026
Same author

Artificial intelligence for predicting depression anxiety and stress using psychometric data.

Scientific reports·2025
Same author

Integrating multi-source data for skin burn classification using deep learning.

Computers in biology and medicine·2025
Same author

The Intersection of Genetics and Neuroimaging: A Systematic Review of Imaging Genetics in Neurological Disease for Personalized Treatment.

Journal of molecular neuroscience : MN·2025

Related Experiment Video

Updated: Aug 29, 2025

Single-stage Dynamic Reanimation of the Smile in Irreversible Facial Paralysis by Free Functional Muscle Transfer
19:53

Single-stage Dynamic Reanimation of the Smile in Irreversible Facial Paralysis by Free Functional Muscle Transfer

Published on: March 1, 2015

106.0K

Classification of facial paralysis based on machine learning techniques.

Amira Gaber1, Mona F Taher2, Manal Abdel Wahed2

  • 1Systems and Biomedical Engineering Department, Faculty of Engineering, Cairo University, Giza, Egypt. amira.gaber@eng1.cu.edu.eg.

Biomedical Engineering Online
|September 7, 2022
PubMed
Summary

A new system uses AI to classify facial paralysis (FP) severity using real-time facial muscle movements captured by Kinect V2. This quantitative approach achieves high accuracy, offering a more objective assessment than current methods.

Keywords:
Ensemble classificationFacial animation unitsFacial paralysisGradingKinectMachine learning

More Related Videos

Facial Nerve Axotomy in Mice: A Model to Study Motoneuron Response to Injury
10:11

Facial Nerve Axotomy in Mice: A Model to Study Motoneuron Response to Injury

Published on: February 23, 2015

13.0K
Facial Nerve Surgery in the Rat Model to Study Axonal Inhibition and Regeneration
05:04

Facial Nerve Surgery in the Rat Model to Study Axonal Inhibition and Regeneration

Published on: May 5, 2020

7.6K

Related Experiment Videos

Last Updated: Aug 29, 2025

Single-stage Dynamic Reanimation of the Smile in Irreversible Facial Paralysis by Free Functional Muscle Transfer
19:53

Single-stage Dynamic Reanimation of the Smile in Irreversible Facial Paralysis by Free Functional Muscle Transfer

Published on: March 1, 2015

106.0K
Facial Nerve Axotomy in Mice: A Model to Study Motoneuron Response to Injury
10:11

Facial Nerve Axotomy in Mice: A Model to Study Motoneuron Response to Injury

Published on: February 23, 2015

13.0K
Facial Nerve Surgery in the Rat Model to Study Axonal Inhibition and Regeneration
05:04

Facial Nerve Surgery in the Rat Model to Study Axonal Inhibition and Regeneration

Published on: May 5, 2020

7.6K

Area of Science:

  • Biomedical Engineering
  • Artificial Intelligence in Healthcare
  • Medical Imaging and Signal Processing

Background:

  • Facial paralysis (FP) significantly impacts daily activities, necessitating objective assessment tools.
  • Current methods for evaluating FP severity lack widespread acceptance and quantitative rigor.
  • A need exists for a reliable system to assess and classify facial paralysis severity.

Purpose of the Study:

  • To develop and test a comprehensive system for quantitative facial paralysis assessment and classification.
  • To evaluate the efficacy of using real-time facial animation units (FAUs) for FP classification.
  • To establish an AI-driven system for classifying FP severity into distinct categories.

Main Methods:

  • Development of a system to extract real-time facial animation units (FAUs) using the Kinect V2 sensor.
  • Compilation of a dataset including patients with unilateral facial paralysis and healthy control subjects.
  • Application of Artificial Intelligence and Machine Learning, specifically an ensemble learning classifier based on Support Vector Machines (SVMs), for classifying seven FP categories.
  • Utilized a hybrid strategy to address dataset imbalance and fivefold cross-validation for robustness assessment.

Main Results:

  • The developed ensemble classifier achieved high prediction performance: 96.8% accuracy, 88.9% sensitivity, and 99% specificity.
  • The system demonstrated robustness and stability across different training and testing samples.
  • Facial animation units (FAUs) acquired via Kinect sensor proved effective for classifying facial paralysis.

Conclusions:

  • The developed system provides a quantitative and objective method for facial paralysis assessment and classification.
  • The AI-based ensemble classifier offers significant advantages over existing subjective grading scales for FP.
  • This technology holds promise for improved diagnosis and management of facial paralysis.