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

Parallel Processing01:20

Parallel Processing

207
The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
207
Fixed Action Patterns01:06

Fixed Action Patterns

16.4K
A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.
16.4K
Muscle Coordination and Action01:24

Muscle Coordination and Action

1.9K
Muscle coordination is a complex and finely tuned process essential for smooth and purposeful movements like flexion, extension, adduction, abduction, and rotation. The human body orchestrates the actions of various muscles working in concert, each with a specific role. Four functional types describe how muscles work together: agonist, antagonist, synergist, and fixator.
Agonists
Agonist muscles, often called prime movers, are the primary muscles responsible for producing a specific movement....
1.9K

You might also read

Related Articles

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

Sort by
Same journal

RETRACTION: Optimization and Modeling of Cr (VI) Removal from Tannery Wastewater onto Activated Carbon Prepared from Coffee Husk and Sulfuric Acid (H<sub>2</sub>SO<sub>4</sub>) as Activating Agent by Using Central Composite Design (CCD).

Journal of environmental and public health·2026
Same journal

RETRACTION: Iodine Status in Pregnant Women Having Urinary Fluoride in Contaminated Areas: A Case Study of Phayao Province.

Journal of environmental and public health·2026
Same journal

RETRACTION: Garden Landscape Design Method in Public Health Urban Planning Based on Big Data Analysis Technology.

Journal of environmental and public health·2025
Same journal

RETRACTION: Analysis and Research on the Impact of Physical Exercise on Residents' Health Based on the Improved BP Neural Network Model.

Journal of environmental and public health·2025
Same journal

RETRACTION: Analysis of the Impact of Ecological Innovation and Green Investment on China's CO<sub>2</sub> Emissions.

Journal of environmental and public health·2025
Same journal

Copyright Transaction Mode and Copyright Protection Risk Analysis of Green Industry from the Perspective of Information Environment.

Journal of environmental and public health·2024
See all related articles

Related Experiment Video

Updated: Aug 29, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K

Dance-Specific Action Recognition Method Based on Double-Stream CNN in Complex Environment.

Yan Jin1

  • 1Shanghai Normal University Music College, Shanghai 200234, China.

Journal of Environmental and Public Health
|September 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a dual-stream convolution neural network for recognizing specific dance movements, improving accuracy and efficiency. This technology enhances dance motion analysis for applications in art, education, and cultural heritage.

More Related Videos

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

615
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K

Related Experiment Videos

Last Updated: Aug 29, 2025

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.5K
Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

615
Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

9.1K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Robotics

Background:

  • Dance-specific motion recognition technology is prevalent across industries, yet Chinese research remains nascent.
  • Accurate recognition of dance movements is crucial for understanding human actions and behaviors.
  • Existing methods face challenges with small sample sizes and accuracy, necessitating improved techniques.

Purpose of the Study:

  • To investigate the recognition of particular dance movements using a novel dual-stream convolution neural network.
  • To address the limitations of current dance motion recognition technologies, particularly concerning accuracy and efficiency.
  • To enhance the practical value and technical complexity of automatic dance motion generation.

Main Methods:

  • A dual-stream convolution neural network architecture was developed and applied to dance movement recognition.
  • The proposed algorithm's performance was evaluated, focusing on recognition accuracy and computational efficiency.
  • Comparative analysis was conducted against existing algorithms (Bergonzoni, 2017; Liu et al., 2021) regarding processing time with varying population densities.

Main Results:

  • The dual-stream convolution neural network demonstrated effectiveness in recognizing specific dance movements.
  • The algorithm exhibited negligible increases in running time with growing population density, outperforming comparative methods.
  • The approach offers a potential solution for the accuracy challenges in dance motion recognition, especially with limited data.

Conclusions:

  • The dual-stream convolution neural network presents a promising advancement for dance-specific motion recognition.
  • This technology has significant implications for art and cultural heritage preservation, dance education, video retrieval, and choreography.
  • Further research is warranted to fully explore the potential of this approach in diverse dance contexts.