A systematic literature review of swimming performance prediction: methods, datasets, techniques and research trends
DOI:
https://doi.org/10.47197/retos.v67.112197Keywords:
Prediction, Artificial Neural Network, Swimming Performance, Method, TrendAbstract
Introduction: Predicting swimming success in competitive sports, primarily the outcomes of forthcoming Olympic swimming competitions.
Objective: This paper provides an extensive and systematic review of research in swimming performance prediction published from 2014 to 2024.
Method: This swimming performance prediction research was conducted with a Systematic Literature Review (SLR). In addition, to create article boundaries by the research topic that was reviewed, this study used projects for systematic reviews and meta-analysis (PRISMA) guidelines to conduct the systematic review. There are 21 journal publications are the result of the extraction of selected studies for identification and analysis to describe research topics or trends, datasets, techniques, methods, evaluations, and problems in this research field. Results: The results of the analysis provide an in-depth explanation of the topics or trends that are the focus of their studies in the field of swimming performance prediction, provide references to public datasets, and explain the techniques and methods that researchers often use to compare and develop methods.
Discussion: A predictive mathematical model is a favourite technique because it integrates complex biologica health , biomechanical variables , providing precise performance prediction. Additionally, to improve both the accuracy and interpretability of predictions need for hybrid approaches that combine mathematical models with more advanced techniques, such as machine learning and explainable artificial intelligence (XAI).
Conclusions: Swimming performance prediction plays a crucial role in enhancing training programs, guiding athlete selection, and evaluating progress.
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