Muhammad Amirul, Abdullah and Muhammad Ar Rahim, Ibrahim and Muhammad Nur Aiman, Shapiee and Mohd Azraai, Mohd Razman and Rabiu Muazu, Musa and Noor Azuan, Abu Osman and Muhammad Aizzat, Zakaria and Anwar, P. P. Abdul Majeed (2023) An evaluation of different input transformation for the classification of skateboarding tricks by means of transfer learning. In: Lecture Notes in Bioengineering; International Conference on Innovation and Technology in Sports, ICITS 2022 , 14 - 15 November 2022 , Kuala Lumpur. 269 -275.. ISSN 2195-271X ISBN 978-981990296-5
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Abstract
This study aims to investigate the effect of different input images, namely raw data (RAW) and Continuous Wavelet Transform (CWT) towards the discriminating of street skateboarding tricks, i.e., Ollie, Kickflip, Shove-it, Nollie and Frontside 180 through a variety of transfer learning with optimised k-Nearest Neighbors (kNN) pipelines. Six amateur skateboarders participated in the study, executed the aforesaid tricks five times per trick on an instrumented skateboard where six time-domain signals were extracted prior it was transformed to RAW and CWT. It was shown from the study that the CWT-InceptionV3-optimised kNN pipeline could attain an average test and validation accuracy of 90%.
| Item Type: | Conference or Workshop Item (Lecture) |
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| Additional Information: | Indexed by Scopus |
| Uncontrolled Keywords: | Classification; k-Nearest Neighbor; Machine learning; Skateboarding; Transfer learning |
| Subjects: | G Geography. Anthropology. Recreation > GV Recreation Leisure Q Science > Q Science (General) T Technology > TA Engineering (General). Civil engineering (General) T Technology > TJ Mechanical engineering and machinery |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Manufacturing and Mechatronic Engineering Technology |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 26 Dec 2023 03:55 |
| Last Modified: | 26 Dec 2023 03:55 |
| URI: | http://umpir.ump.edu.my/id/eprint/39755 |
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