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

You might also read

Related Articles

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

Sort by
Same author

A Model for Analyzing Teaching Quality Data of Sports Faculties Based on Particle Swarm Optimization Neural Network.

Computational intelligence and neuroscience·2022
Same journal

RETRACTION: Real-Time Modulation of Physical Training Intensity Based on Wavelet Recursive Fuzzy Neural Networks.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
See all related articles

Related Experiment Video

Updated: Oct 26, 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

Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural

Xue Li1

  • 1Department of Physical Education, Xi'an International Studies University, Xi'an 710128, Shaanxi, China.

Computational Intelligence and Neuroscience
|July 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning system using Long Short-Term Memory (LSTM) recurrent neural networks for efficient human motion recognition. The proposed system significantly improves accuracy and reduces data loss compared to traditional methods.

More Related Videos

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.9K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K

Related Experiment Videos

Last Updated: Oct 26, 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
Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.9K
Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.3K

Area of Science:

  • Sports Science
  • Biomedical Engineering
  • Artificial Intelligence

Background:

  • The growing national fitness movement in China necessitates advanced human movement analysis.
  • Existing human motion recognition systems suffer from low efficiency and poor data reduction capabilities.
  • There is a need for improved systems to accurately collect, process, and recognize human motion data.

Purpose of the Study:

  • To develop and evaluate a novel human motion recognition information processing system.
  • To enhance recognition efficiency and reduce data missing rates using deep learning.
  • To apply the system in physical education and daily physical exercise contexts.

Main Methods:

  • A three-layer human motion recognition information processing system was constructed.
  • Long Short-Term Memory (LSTM) recurrent neural networks were integrated for optimized recognition.
  • The system was trained and validated using a known dataset and real-world motion data.

Main Results:

  • The LSTM-based system demonstrated superior performance over traditional algorithms, achieving an accuracy of 0.980.
  • Confusion matrix analysis indicated a maximum recognition score of 85 for human motion.
  • The system effectively recognized and processed human movement data in tests.

Conclusions:

  • The proposed LSTM recurrent neural network system offers a significant advancement in human motion recognition.
  • The system's high accuracy and efficiency have substantial implications for physical education and fitness.
  • This technology holds promise for future applications in sports science and rehabilitation.