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Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach

  • S.I. : Deep Neuro-Fuzzy Analytics in Smart Ecosystems
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Abstract

Athletes represent the apex of physical capacity filling in a social picture of performance and build. In light of the fundamental contrasts in athletic capacities required for different games, each game demands an alternate body type standard. Because of the decent variety of these body types, each can have an altogether different body standard. Nowadays, a large number of athletes participate in assessments and a large number of human hours are spent on playing out these assessments every year. These assessments are performed to check the physical strength of athletes and evaluate them for different games. This paper presents a machine learning approach to the physical assessment of athletes known as NueroFATH. The proposed NueroFATH approach relies on neuro-fuzzy analytics that involves the deployment of neural networks and fuzzy c-means techniques to predict the athletes for the potential of winning medals. This can be achieved using athletes’ physical assessment parameters. The goal of this study is not only to identify the athletes based on which group they fall into (gold/silver/bronze), but also to understand which physical characteristic is important to identify them and categorize them in a medal group. It was determined that features, namely height, body mass, body mass index, 40 m and vertical jump are the most important for achieving 98.40% accuracy for athletes to classify them in the gold category when they are in the bronze category. Unsupervised learning showed that features, namely body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity have the highest variability. We can achieve upto 97.06% accuracy when features, i.e., body mass, body mass index, vertical jump, med ball, 40 m, peak oxygen content, peak height velocity were used.

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Acknowledgements

Authors are thankful to Aspire Academy for providing the dataset which is used in this research study. This work was made possible by NPRP grant # NPRP12S-0305-190231 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the authors.

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Authors and Affiliations

  1. Department of Computer Science, University of Texas, San Antonio, TX, USA

    Heena Rathore

  2. Department of Computer Science and Engineering, Qatar University, Doha, Qatar

    Amr Mohamed & Mohsen Guizani

  3. School of Engineering, Computing and Mathematics, University of Plymouth, Plymouth, UK

    Shailendra Rathore

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  1. Heena Rathore
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  2. Amr Mohamed
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  3. Mohsen Guizani
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  4. Shailendra Rathore
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Correspondence to Heena Rathore.

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Rathore, H., Mohamed, A., Guizani, M. et al. Neuro-fuzzy analytics in athlete development (NueroFATH): a machine learning approach. Neural Comput & Applic 35, 23697–23710 (2023). https://doi.org/10.1007/s00521-021-05704-5

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  • DOI: https://doi.org/10.1007/s00521-021-05704-5

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