Aina Munirah, Ab Rasid and Muhammad Zuhaili, Suhaimi and Anwar, P. P. Abdul Majeed and Mohd Azraai, Mohd Razman and Mohd Hasnun Ariff, Hassan and Nasree, Najmi and Noor Azuan, Abu Osman and Rabiu Muazu, Musa (2023) A cluster analysis and artificial neural network of identifying skateboarding talents based on bio-fitness indicators. In: Innovation and Technology in Sports. Lecture Notes in Bioengineering; International Conference on Innovation and Technology in Sports, ICITS 2022 , 14 - 15 November 2022 , Kuala Lumpur. 47 -56.. ISSN 2195-271X ISBN 978-981990296-5
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Abstract
This research aims to identify talented skateboarding athletes with reference to their bio-fitness indicators. A total of 45 skateboarders (23.09 ± 5.41 years) who were playing for recreational purposes were recruited for the study. Standard assessment of their bio-fitness as well as their skateboarding performances was performed. The bio-fitness investigated consisted of stork balance, star excursion balance test, vertical jump, standing broad jump, single-leg wall sits, plank and sit-up while the related-skill performances consisted of the observation on skateboarding tricks execution, namely Ollie, Nollie, Frontside 180, Pop-Shuvit and Kickflip. To achieve the objective of the study, a hierarchical agglomerative cluster analysis (HACA) was performed to cluster the athletes into groups in reference to the level of their bio-fitness markers. The clusters identified two groups of performance named High-Potential Skaters (HPS) and Low-Potential Skaters (LPS) following their skateboarding performance scores. An Artificial Neural Network (ANN) was conducted to ascertain the classified athletes into the clusters (HPS and LPS) based on the bio-fitness indicators evaluated along with the skateboarding tricks performance scores. The result demonstrated that ANN accomplished a high classification accuracy of 91.7% indicating excellent performance from the classifier in classifying the skateboarding athletes. Similarly, the area under the curve of the classifier was found to be 0.988 signifying further the validity of the model developed. Overall, these results suggest that the proposed technique was able to classify the skateboarding athletes reasonably well which will in turn possibly assist coaches to identify talents in this sport through the bio-fitness indicators examined.
| Item Type: | Conference or Workshop Item (Lecture) |
|---|---|
| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Bio-fitness; Hierarchical Agglomerative Clustering; Individual extreme sport; Machine learning; Skateboarding; Talent identification |
| Subjects: | T Technology > TJ Mechanical engineering and machinery |
| Faculty/Division: | Faculty of Manufacturing and Mechatronic Engineering Technology Faculty of Mechanical and Automotive Engineering Technology |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 26 Dec 2023 03:06 |
| Last Modified: | 26 Dec 2023 03:06 |
| URI: | http://umpir.ump.edu.my/id/eprint/39749 |
| Download Statistic: | View Download Statistics |
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