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

Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

340
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
340

You might also read

Related Articles

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

Sort by
Same author

Combining Engineered Probe Library with Tiered Screening for the Rational Discovery of Alzheimer's Diagnostic Probe with an Enhanced Signal-to-Noise Ratio.

Analytical chemistry·2026
Same author

Intensity-Texture Enhanced Swin Fusion for Bacterial Contamination Detection in <i>Alocasia</i> Explants.

Sensors (Basel, Switzerland)·2026
Same author

Associations Between Comorbidities, Developmental Status, and Disease Severity in Children With Autism Spectrum Disorder: A Multicenter Cross-Sectional Study in China.

Autism research : official journal of the International Society for Autism Research·2026
Same author

YOLO-APLD: A Lightweight Apple Leaf Disease Detection Model Based on Multiscale Feature Fusion.

Plant disease·2025
Same author

Aero-Engine Ablation Defect Detection with Improved CLR-YOLOv11 Algorithm.

Sensors (Basel, Switzerland)·2025
Same author

Research on a Method for Identification of Chinese Rose Leaf Pests and Diseases Based on a Lightweight CR-YOLO Model.

Plant disease·2025
Same journal

From silenced shock to strategic resilience: a longitudinal qualitative study of nurse residents' trajectory in coping with patient verbal abuse.

Frontiers in psychology·2026
Same journal

Validation of the Internet Addiction Test (IAT) for forest firefighters: implications for human-technology interaction and occupational safety in the future of work.

Frontiers in psychology·2026
Same journal

Development and validation of the football emotion scale for Chinese youth players: a psychometric study.

Frontiers in psychology·2026
Same journal

From online engagement to offline action: how social media environmental engagement shapes university students' pro-environmental citizenship through intrinsic motivation and personal norms.

Frontiers in psychology·2026
Same journal

The multidimensional inventory of religious/spiritual wellbeing in Hungarian language: psychometric properties and initial validation.

Frontiers in psychology·2026
Same journal

Effects of occupational factors on depression in Chinese veterans: a fsQCA study based on 2022 CFPS data.

Frontiers in psychology·2026
See all related articles

Related Experiment Video

Updated: Oct 29, 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

4.6K

Convolutional Neural Network-Based Human Movement Recognition Algorithm in Sports Analysis.

Jiatian Liu1

  • 1College of Strength and Conditioning, Beijing Sport University, Beijing, China.

Frontiers in Psychology
|July 12, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a human action recognition (HAR) algorithm using a convolutional neural network (CNN) to analyze athlete psychology in sports. The optimized model achieves 80% accuracy in recognizing actions, aiding sports psychology research.

Keywords:
convolutional neural networkhuman action recognitionimage recognitionsports analysissports psychology

More Related Videos

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

9.6K
Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography
04:06

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

Published on: January 12, 2024

794

Related Experiment Videos

Last Updated: Oct 29, 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

4.6K
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

9.6K
Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography
04:06

Author Spotlight: Enhancing Remote Rehabilitation with Virtual Reality and Electromyography

Published on: January 12, 2024

794

Area of Science:

  • Sports Science
  • Computer Vision
  • Psychology

Background:

  • Analyzing athlete psychology is crucial for understanding sports performance.
  • Human action recognition (HAR) offers a novel approach to studying athlete behavior.
  • Existing methods may lack the precision needed for nuanced psychological analysis in sports.

Purpose of the Study:

  • To develop and evaluate a HAR algorithm for analyzing athlete psychology during movements.
  • To investigate the psychological aspects of basketball players' deceptive actions.
  • To predict athletes' subsequent actions based on movement analysis.

Main Methods:

  • A convolutional neural network (CNN) was employed to build the HAR model.
  • The model analyzed action information from collected videos to classify current states.
  • A combination of grayscale and red-green-blue (RGB) images was used to enhance recognition accuracy.

Main Results:

  • The optimized convolutional 3D network (C3D) HAR model achieved 80% recognition accuracy.
  • Image loss was reduced to 5.6 by combining grayscale and RGB images.
  • Time complexity was reduced by 33% compared to baseline models.

Conclusions:

  • The optimized C3D HAR model effectively recognizes human actions in sports.
  • This approach provides valuable insights into sports psychology and athlete behavior analysis.
  • The findings can inform future research in sports-related image recognition and psychological profiling.