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

Muscles for Facial Expressions

Muscles for Facial Expressions

The craniofacial muscles are a collection of approximately 20 thin skeletal muscles situated beneath the skin of the face and scalp. These muscles, primarily responsible for the vast array of human facial expressions, originate from the bones or fibrous structures of the skull and extend outwards to connect with the skin. While most skeletal muscles in the body are enveloped in thick fascia, facial muscles generally have a more delicate fascial covering, with the buccinator muscle being a...

You might also read

Related Articles

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

Sort by

Same author

Understanding sleep and smartphone use in diverse adolescents through passive digital monitoring.

Journal of research on adolescence : the official journal of the Society for Research on Adolescence·2026

Same author

Medical evaluation of first presentation of psychotic symptoms in children and adolescents.

Frontiers in psychiatry·2026

Same author

A topographical organization in the primary olfactory cortex.

Nature communications·2026

Same author

Animal models of Gilles de la Tourette Syndrome and other tic disorders.

Handbook of clinical neurology·2026

Same author

Home-based psychiatric hospitalization for children and adolescents during war times.

Psychiatry research·2025

Same author

Protecting children in crisis: An integrated approach to mass trauma response - clinical innovations, systems coordination, and evidence-based interventions.

Psychiatry research·2025

Same journal

AD-DAE: Alzheimer's Disease Progression Modeling with Unpaired Longitudinal MRI using Diffusion Auto-Encoders.

IEEE journal of biomedical and health informatics·2026

Same journal

EEG Connectivity Signatures in Active vs. Passive Mental Fatigue Settings.

IEEE journal of biomedical and health informatics·2026

Same journal

Privacy-Enhanced Vertical Federated Learning for Healthcare via Directional Noise and Subset Representations.

IEEE journal of biomedical and health informatics·2026

Same journal

Multimodal Bidirectional Direct Preference Optimization and Instruction Fine-Tuning for Medical Image Understanding and Generation.

IEEE journal of biomedical and health informatics·2026

Same journal

CT: A Controllable Transformer for Multi-Task TCM Facial Inspection.

IEEE journal of biomedical and health informatics·2026

Same journal

Marfan Syndrome Prediction Via Graph Neural Networks on 3D Facial Cues.

IEEE journal of biomedical and health informatics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 9, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Automatic Identification of Facial Tics Using Selfie-Video.

Yocheved Loewenstern, Noa Benaroya-Milshtein, Katya Belelovsky

IEEE Journal of Biomedical and Health Informatics

|October 30, 2024

Summary

This summary is machine-generated.

This study developed an objective smartphone tool to automatically detect facial motor tics in children and adolescents with tic disorders. The AI model achieved over 90% accuracy, offering a promising method for continuous tic assessment.

More Related Videos

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Related Experiment Videos

Last Updated: Jun 9, 2025

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease

Published on: July 24, 2019

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

Published on: August 26, 2016

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

Area of Science:

Neuroscience
Biomedical Engineering
Computational Psychiatry

Background:

Tic disorders present with variable symptoms, complicating clinical assessment.
Current tic evaluations rely on subjective, infrequent questionnaires.
Objective, continuous monitoring of tic expression is needed.

Purpose of the Study:

To develop an automated, objective method for measuring facial motor tics.
To utilize smartphone technology for tic assessment in naturalistic settings.
To improve diagnosis, monitoring, and treatment evaluation for tic disorders.

Main Methods:

A custom smartphone app recorded selfie-videos of participants with tic disorders.
Facial landmarks were used to extract tic-related features from video segments.
Deep neural networks analyzed spatial and temporal features for tic classification.

Main Results:

The developed model achieved a mean accuracy of 95% across all subjects.
Cross-validation schemes (leave-one-session-out, leave-one-subject-out) exceeded 90% accuracy.
The system demonstrated robust performance in identifying tic expressions.

Conclusions:

This automatic tic identification system offers a valuable, objective clinical tool.
It facilitates diagnosis, patient follow-up, and treatment efficacy assessment.
Integration with smartphone technology can transform clinical studies and intervention development.