An evaluation of different input transformation for the classification of skateboarding tricks by means of transfer learning

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...

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Main Authors: Muhammad Amirul, Abdullah, Muhammad Ar Rahim, Ibrahim, Muhammad Nur Aiman, Shapiee, Mohd Azraai, Mohd Razman, Rabiu Muazu, Musa, Noor Azuan, Abu Osman, Muhammad Aizzat, Zakaria, Anwar, P. P. Abdul Majeed
Format: Conference or Workshop Item
Language:English
Published: Springer 2023
Subjects:
Online Access:http://umpir.ump.edu.my/id/eprint/39755/
http://umpir.ump.edu.my/id/eprint/39755/1/An%20evaluation%20of%20different%20input%20transformation%20for%20the%20classification%20of%20skateboarding%20.pdf
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author Muhammad Amirul, Abdullah
Muhammad Ar Rahim, Ibrahim
Muhammad Nur Aiman, Shapiee
Mohd Azraai, Mohd Razman
Rabiu Muazu, Musa
Noor Azuan, Abu Osman
Muhammad Aizzat, Zakaria
Anwar, P. P. Abdul Majeed
author_facet Muhammad Amirul, Abdullah
Muhammad Ar Rahim, Ibrahim
Muhammad Nur Aiman, Shapiee
Mohd Azraai, Mohd Razman
Rabiu Muazu, Musa
Noor Azuan, Abu Osman
Muhammad Aizzat, Zakaria
Anwar, P. P. Abdul Majeed
author_sort Muhammad Amirul, Abdullah
building UMP Institutional Repository
collection Online Access
description 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%.
first_indexed 2025-11-15T03:35:36Z
format Conference or Workshop Item
id ump-39755
institution Universiti Malaysia Pahang
institution_category Local University
language English
last_indexed 2025-11-15T03:35:36Z
publishDate 2023
publisher Springer
recordtype eprints
repository_type Digital Repository
spelling ump-397552023-12-26T03:55:03Z http://umpir.ump.edu.my/id/eprint/39755/ An evaluation of different input transformation for the classification of skateboarding tricks by means of transfer learning Muhammad Amirul, Abdullah Muhammad Ar Rahim, Ibrahim Muhammad Nur Aiman, Shapiee Mohd Azraai, Mohd Razman Rabiu Muazu, Musa Noor Azuan, Abu Osman Muhammad Aizzat, Zakaria Anwar, P. P. Abdul Majeed GV Recreation Leisure Q Science (General) TA Engineering (General). Civil engineering (General) TJ Mechanical engineering and machinery 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%. Springer 2023 Conference or Workshop Item PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39755/1/An%20evaluation%20of%20different%20input%20transformation%20for%20the%20classification%20of%20skateboarding%20.pdf 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 (Published) https://doi.org/10.1007/978-981-99-0297-2_22
spellingShingle GV Recreation Leisure
Q Science (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
Muhammad Amirul, Abdullah
Muhammad Ar Rahim, Ibrahim
Muhammad Nur Aiman, Shapiee
Mohd Azraai, Mohd Razman
Rabiu Muazu, Musa
Noor Azuan, Abu Osman
Muhammad Aizzat, Zakaria
Anwar, P. P. Abdul Majeed
An evaluation of different input transformation for the classification of skateboarding tricks by means of transfer learning
title An evaluation of different input transformation for the classification of skateboarding tricks by means of transfer learning
title_full An evaluation of different input transformation for the classification of skateboarding tricks by means of transfer learning
title_fullStr An evaluation of different input transformation for the classification of skateboarding tricks by means of transfer learning
title_full_unstemmed An evaluation of different input transformation for the classification of skateboarding tricks by means of transfer learning
title_short An evaluation of different input transformation for the classification of skateboarding tricks by means of transfer learning
title_sort evaluation of different input transformation for the classification of skateboarding tricks by means of transfer learning
topic GV Recreation Leisure
Q Science (General)
TA Engineering (General). Civil engineering (General)
TJ Mechanical engineering and machinery
url http://umpir.ump.edu.my/id/eprint/39755/
http://umpir.ump.edu.my/id/eprint/39755/
http://umpir.ump.edu.my/id/eprint/39755/1/An%20evaluation%20of%20different%20input%20transformation%20for%20the%20classification%20of%20skateboarding%20.pdf