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

Muscles for Facial Expressions01:14

Muscles for Facial Expressions

2.2K
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...
2.2K
Facial Feedback Hypothesis01:24

Facial Feedback Hypothesis

179
Charles Darwin proposed that facial expressions are an evolutionary adaptation for communication. He argued that these expressions are not influenced by culture but are universal across species. For example, a snarling expression with exposed teeth signals a threat in many animals, including humans. Darwin also suggested that displaying an emotion can intensify the feeling. Smiling, for example, could enhance one's sense of happiness. This idea laid the foundation for understanding the role...
179
Prosopagnosia01:24

Prosopagnosia

198
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...
198

You might also read

Related Articles

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

Sort by
Same author

The social dimension of apathy: evidence for a distinct domain from 11,243 individuals across health and neurocognitive disorders.

Translational psychiatry·2026
Same author

Characterising a stress-sensitive default mode network (DMN) deficit in major psychiatric disorders.

Communications biology·2026
Same author

Remote digital cognitive assessment for aging and dementia using the Oxford Cognitive Testing Portal OCTAL.

NPJ digital medicine·2026
Same author

The Potential for Smart Glasses to Transform Facial Palsy Therapy Globally: UK Budget Analysis, Delphi Outcomes Valuation Exercise, and Economic Modeling of Cost-Effectiveness.

Journal of medical Internet research·2025
Same author

Validating OCOsense smart glasses in a three-week home-based study: Assessing detection of eating, food identification and the use of haptic feedback to aid behaviour modification.

Appetite·2025
Same author

Prevention and rehabilitation of facial palsy in patients with vestibular schwannomas.

Handbook of clinical neurology·2025

Related Experiment Video

Updated: Jul 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

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

Published on: December 15, 2023

3.8K

Towards smart glasses for facial expression recognition using OMG and machine learning.

Ivana Kiprijanovska1, Simon Stankoski2, M John Broulidakis2

  • 1Emteq Ltd., Brighton, BN1 9SB, UK. ivana.kiprijanovska@emteqlabs.com.

Scientific Reports
|September 25, 2023
PubMed
Summary

Novel smart glasses using optomyography (OMG) accurately detect facial expressions like smiling and frowning. Head movements do not significantly affect detection, achieving 93% accuracy in real-world simulations.

More Related Videos

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
07:47

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

11.3K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

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

Published on: March 28, 2025

587

Related Experiment Videos

Last Updated: Jul 15, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

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

Published on: December 15, 2023

3.8K
Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification
07:47

Design and Evaluation of Smart Glasses for Food Intake and Physical Activity Classification

Published on: February 14, 2018

11.3K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

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

Published on: March 28, 2025

587

Area of Science:

  • Biomedical Engineering
  • Human-Computer Interaction
  • Wearable Technology

Background:

  • Facial expression recognition is crucial for human-computer interaction and affective computing.
  • Existing methods often struggle with real-world conditions like head movement and varying expression intensity.
  • Optomyography (OMG) offers a potential non-invasive approach for facial movement monitoring.

Purpose of the Study:

  • To evaluate the efficacy of OCOsense smart glasses, utilizing optomyography (OMG), for monitoring and recognizing facial expressions.
  • To assess the impact of head movement and expression variations on detection accuracy.
  • To develop and validate a machine learning model for real-time facial expression recognition using OMG data.

Main Methods:

  • 27 young adults performed various facial expressions (smiling, frowning, eyebrow raise, eye squeeze) with controlled intensity, duration, and head movement.
  • OCOsense smart glasses equipped with OMG sensors captured facial muscle activity.
  • A machine learning model was trained and evaluated using leave-one-subject-out cross-validation under simulated real-world conditions.

Main Results:

  • OCO sensors demonstrated high accuracy and specificity in capturing distinct cheek and brow movements.
  • Head movement did not significantly impede the detection of facial expressions.
  • The machine learning model achieved an overall accuracy of 93% (0.90 f1-score) for recognizing four expressions and a neutral state.

Conclusions:

  • Optomyography-based smart glasses (OCOsense) provide a robust and accurate method for facial expression monitoring.
  • The system is resilient to head movements and variations in expression intensity, suggesting practical applicability.
  • This technology holds promise for applications in affective computing, assistive technologies, and beyond.