ArticleSensors (Basel, Switzerland)2020
Using Artificial Intelligence for Pattern Recognition in a Sports Context.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Application of digital-intelligent technologies in physical education: a systematic review.Frontiers in public health · 2025Pooled it
- A Smart Ski Pole for Skiing Pattern Recognition and Quantification Application.Sensors (Basel, Switzerland) · 2024Article
- Artificial Intelligence Program for Predicting Wrestlers' Sports Performances.Sports (Basel, Switzerland) · 2023Article
- Artificial Intelligence Approach in Biomechanics of Gait and Sport: A Systematic Literature Review.Journal of biomedical physics & engineering · 2023Review
- Human-Aware Collaborative Robots in the Wild: Coping with Uncertainty in Activity Recognition.Sensors (Basel, Switzerland) · 2023Article
- Using Artificial Intelligence-Enhanced Sensing and Wearable Technology in Sports Medicine and Performance Optimisation.Sensors (Basel, Switzerland) · 2022Review
- A Deep Learning and Clustering Extraction Mechanism for Recognizing the Actions of Athletes in Sports.Computational intelligence and neuroscience · 2022Article
- Sports Action Recognition Based on Deep Learning and Clustering Extraction Algorithm.Computational intelligence and neuroscience · 2022Article
- Optimization of a Sports Activity Development Model Using Artificial Intelligence under New Curriculum Reform.International journal of environmental research and public health · 2021Article
- Using Optical Tracking System Data to Measure Team Synergic Behavior: Synchronization of Player-Ball-Goal Angles in a Football Match.Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Optimizing athlete's performance is one of the most important and challenging aspects of coaching. Physiological and positional data, often acquired using wearable devices, have been useful to identify patterns, thus leading to a better understanding of the game and, consequently, providing the opportunity to improve the athletic performance. Even though there is a panoply of research in pattern recognition, there is a gap when it comes to non-controlled environments, as during sports training and competition. This research paper combines the use of physiological and positional data as sequential features of different artificial intelligence approaches for action recognition in a real match context, adopting futsal as its case study. The traditional artificial neural networks (ANN) is compared with a deep learning method, Long Short-Term Memory Network, and also with the Dynamic Bayesian Mixture Model, which is an ensemble classification method. The methods were used to process all data sequences, which allowed to determine, based on the balance between precision and recall, that Dynamic Bayesian Mixture Model presents a superior performance, with an F1 score of 80.54% against the 33.31% achieved by the Long Short-Term Memory Network and 14.74% achieved by ANN.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.