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

Social Facilitation01:04

Social Facilitation

35.9K
Not all intergroup interactions lead to negative outcomes. Sometimes, being in a group situation can improve performance. Social facilitation occurs when an individual performs better when an audience is watching than when the individual performs the behavior alone. This typically occurs when people are performing a task for which they are skilled.
35.9K

You might also read

Related Articles

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

Sort by
Same author

Goldfit Soccer: A Multidimensional Model for Talent Identification of Young Soccer Players.

Research quarterly for exercise and sport·2024
Same author

Cause-Effect: The Relationship between Role and Experience with Psychological and Physical Responses in the Competition Context in Soccer Referees.

Journal of human kinetics·2023
Same author

Exploring dual career quality implementation at European higher education institutions: Insights from university experts.

PloS one·2022
Same author

Knee and hip agonist-antagonist relationship in male under-19 soccer players.

PloS one·2022
Same author

Understanding dual career views of European university athletes: The more than gold project focus groups.

PloS one·2022
Same author

Maturation, signal detection, and tactical behavior of young soccer players in the game context.

Science & medicine in football·2022
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Dec 20, 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

5.1K

Using Artificial Intelligence for Pattern Recognition in a Sports Context.

Ana Cristina Nunes Rodrigues1, Alexandre Santos Pereira2, Rui Manuel Sousa Mendes3

  • 1Coimbra Polytechnic, Instituto Superior de Engenharia de Coimbra, 3030-199 Coimbra, Portugal.

Sensors (Basel, Switzerland)
|May 31, 2020
PubMed
Summary
This summary is machine-generated.

This study used AI to analyze athlete performance in real futsal matches. The Dynamic Bayesian Mixture Model best recognized actions using physiological and positional data, outperforming other AI methods.

Keywords:
artificial intelligenceartificial neural networkensemble classification methodlong short-term memorysportswearable technology

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

789
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

9.5K

Related Experiment Videos

Last Updated: Dec 20, 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

5.1K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

789
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

9.5K

Area of Science:

  • Sports Science
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Optimizing athlete performance is crucial in coaching, with physiological and positional data aiding game understanding.
  • Existing pattern recognition research often lacks application in uncontrolled sports environments like training and competition.

Purpose of the Study:

  • To apply artificial intelligence (AI) for action recognition using sequential physiological and positional data in real-world futsal matches.
  • To compare the effectiveness of traditional Artificial Neural Networks (ANN), Long Short-Term Memory Networks (LSTM), and Dynamic Bayesian Mixture Models (DBMM) for this task.

Main Methods:

  • Collected and processed sequential physiological and positional data from futsal players during matches.
  • Implemented and compared three AI approaches: ANN, LSTM, and DBMM for action recognition.
  • Evaluated model performance based on the balance between precision and recall, using the F1 score.

Main Results:

  • The Dynamic Bayesian Mixture Model (DBMM) demonstrated superior performance in action recognition.
  • DBMM achieved an F1 score of 80.54%, significantly outperforming LSTM (33.31%) and ANN (14.74%).
  • The study highlights the effectiveness of ensemble methods in complex, real-world sports data analysis.

Conclusions:

  • The Dynamic Bayesian Mixture Model is highly effective for action recognition in sports using combined physiological and positional data.
  • AI, particularly ensemble methods, can significantly enhance the understanding and optimization of athlete performance in dynamic, uncontrolled environments.
  • This research provides a robust framework for applying advanced AI techniques to sports analytics.