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

4.0K
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...
4.0K
Somatic Spinal Reflexes01:22

Somatic Spinal Reflexes

3.8K
Somatic spinal reflexes are rapid, involuntary muscular responses to external stimuli that involve the somatic musculature and the spinal cord.
One of the most well-known somatic spinal reflexes is the stretch reflex, which is activated by the sudden stretching of a muscle. This reflex involves the activation of specialized sensory receptors called muscle spindles, which are located in the muscle tissue and detect changes in the length and speed of muscle contractions. When a muscle is suddenly...
3.8K

You might also read

Related Articles

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

Sort by
Same author

The Role of Selected Speech Signal Characteristics in Discriminating Unipolar and Bipolar Disorders.

Sensors (Basel, Switzerland)·2024
Same author

Deep Learning for 3D Reconstruction, Augmentation, and Registration: A Review Paper.

Entropy (Basel, Switzerland)·2024
Same author

Processing emotions from faces and words measured by event-related brain potentials.

Cognition & emotion·2023
Same author

Towards Context-Aware Facial Emotion Reaction Database for Dyadic Interaction Settings.

Sensors (Basel, Switzerland)·2023
Same author

A Comparison of Neural Networks and Center of Gravity in Muon Hit Position Estimation.

Entropy (Basel, Switzerland)·2022
Same author

Comprehensive Empirical Evaluation of Deep Learning Approaches for Session-Based Recommendation in E-Commerce.

Entropy (Basel, Switzerland)·2022

Related Experiment Video

Updated: Nov 27, 2025

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
11:31

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

15.5K

Emotion Recognition from Skeletal Movements.

Tomasz Sapiński1, Dorota Kamińska1, Adam Pelikant1

  • 1Institute of Mechatronics and Information Systems Lodz University of Technology, 90-924 Lodz, Poland.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for automatic emotion recognition using body movements. The research demonstrates the feasibility of recognizing seven basic emotions from body gestures, enhancing multimodal emotion recognition systems.

Keywords:
Kinect sensorbody movementsdeep learningemotion recognitiongesturesneural networks

More Related Videos

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

996
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.8K

Related Experiment Videos

Last Updated: Nov 27, 2025

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
11:31

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

15.5K
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

996
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

43.8K

Area of Science:

  • Artificial Intelligence
  • Affective Computing
  • Human-Computer Interaction

Background:

  • Automated emotion recognition is a growing field in AI.
  • Current methods primarily focus on facial expressions and speech.
  • Body movement as an emotional indicator is underutilized in automated analysis.

Purpose of the Study:

  • To propose a novel method for recognizing seven basic emotional states using body movement.
  • To explore the potential of body gestures as a data source for emotion recognition.
  • To develop a sequential model for affective movement analysis.

Main Methods:

  • Collected motion capture data of seven basic emotions (happy, sad, surprise, fear, anger, disgust, neutral) using a Microsoft Kinect v2 sensor.
  • Developed a new representation for affective movements based on sequences of body joints.
  • Extracted low-level features from joint spatial locations and orientations to create a sequential model.
  • Employed and compared various deep neural networks for emotion recognition from motion sequences.

Main Results:

  • The proposed method successfully recognized seven basic emotional states from body movement sequences.
  • Experimental results validated the feasibility of using body gestures for emotion recognition.
  • Deep neural networks effectively classified emotional states based on the proposed movement representation.

Conclusions:

  • Automatic emotion recognition from body gestures is feasible and effective.
  • Body movement analysis can serve as a valuable supplementary data source in multimodal emotion recognition systems.
  • This approach offers a new avenue for enhancing AI-based emotion understanding.