Classification of skateboarding tricks by synthesizing transfer learning models and machine learning classifiers using different input signal transformations

Skateboarding has made its Olympic debut at the delayed Tokyo 2020 Olympic Games. Conventionally, in the competition scene, the scoring of the game is done manually and subjectively by the judges through the observation of the trick executions. Nevertheless, the complexity of the manoeuvres executed...

Full description

Saved in:

Bibliographic Details
Main Author:	Muhammad Amirul, Abdullah
Format:	Thesis
Language:	English
Published:	2022
Subjects:	TJ Mechanical engineering and machinery TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/35919/1/06.Classification%20of%20skateboarding%20tricks%20by%20synthesizing%20transfer%20learning%20models%20and%20machine%20learning%20classifiers.pdf
Tags:	Add Tag No Tags, Be the first to tag this record!

id	my-ump-ir.35919
record_format	uketd_dc
spelling	my-ump-ir.359192023-11-01T07:05:55Z Classification of skateboarding tricks by synthesizing transfer learning models and machine learning classifiers using different input signal transformations 2022-03 Muhammad Amirul, Abdullah TJ Mechanical engineering and machinery TS Manufactures Skateboarding has made its Olympic debut at the delayed Tokyo 2020 Olympic Games. Conventionally, in the competition scene, the scoring of the game is done manually and subjectively by the judges through the observation of the trick executions. Nevertheless, the complexity of the manoeuvres executed has caused difficulties in its scoring that is obviously prone to human error and bias. Therefore, the aim of this study is to classify five skateboarding flat ground tricks which are Ollie, Kickflip, Shove-it, Nollie and Frontside 180. This is achieved by using three optimized machine learning models of k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Machine (SVM) from features extracted via eighteen transfer learning models. Six amateur skaters performed five tricks on a customized ORY skateboard. The raw data from the inertial measurement unit (IMU) embedded on the developed device attached to the skateboarding were extracted. It is worth noting that four types of input images were transformed via Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and synthesized raw image (RAW) from the IMU-based signals obtained. The optimized form of the classifiers was obtained by performing GridSearch optimization technique on the training dataset with 3-folds cross-validation on a data split of 4:1:1 ratio for training, validation and testing, respectively from 150 transformed images. It was shown that the CWT and RAW images used in the MobileNet transfer learning model coupled with the optimized SVM and RF classifiers exhibited a test accuracy of 100%. In order to identify the best possible method for the pipelines, computational time was used to evaluate the various models. It was concluded that the RAW-MobileNet-optimized-RF approach was the most effective one, with a computational time of 24.796875 seconds. The results of the study revealed that the proposed approach could improve the classification of skateboarding tricks. 2022-03 Thesis http://umpir.ump.edu.my/id/eprint/35919/ http://umpir.ump.edu.my/id/eprint/35919/1/06.Classification%20of%20skateboarding%20tricks%20by%20synthesizing%20transfer%20learning%20models%20and%20machine%20learning%20classifiers.pdf pdf en public phd doctoral Universiti Malaysia Pahang Faculty of Manufacturing and Mechatronic Engineering Technology Abdul Majeed, Anwar P.P
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
advisor	Abdul Majeed, Anwar P.P
topic	TJ Mechanical engineering and machinery TS Manufactures
spellingShingle	TJ Mechanical engineering and machinery TS Manufactures Muhammad Amirul, Abdullah Classification of skateboarding tricks by synthesizing transfer learning models and machine learning classifiers using different input signal transformations
description	Skateboarding has made its Olympic debut at the delayed Tokyo 2020 Olympic Games. Conventionally, in the competition scene, the scoring of the game is done manually and subjectively by the judges through the observation of the trick executions. Nevertheless, the complexity of the manoeuvres executed has caused difficulties in its scoring that is obviously prone to human error and bias. Therefore, the aim of this study is to classify five skateboarding flat ground tricks which are Ollie, Kickflip, Shove-it, Nollie and Frontside 180. This is achieved by using three optimized machine learning models of k-Nearest Neighbor (kNN), Random Forest (RF), and Support Vector Machine (SVM) from features extracted via eighteen transfer learning models. Six amateur skaters performed five tricks on a customized ORY skateboard. The raw data from the inertial measurement unit (IMU) embedded on the developed device attached to the skateboarding were extracted. It is worth noting that four types of input images were transformed via Fast Fourier Transform (FFT), Continuous Wavelet Transform (CWT), Discrete Wavelet Transform (DWT) and synthesized raw image (RAW) from the IMU-based signals obtained. The optimized form of the classifiers was obtained by performing GridSearch optimization technique on the training dataset with 3-folds cross-validation on a data split of 4:1:1 ratio for training, validation and testing, respectively from 150 transformed images. It was shown that the CWT and RAW images used in the MobileNet transfer learning model coupled with the optimized SVM and RF classifiers exhibited a test accuracy of 100%. In order to identify the best possible method for the pipelines, computational time was used to evaluate the various models. It was concluded that the RAW-MobileNet-optimized-RF approach was the most effective one, with a computational time of 24.796875 seconds. The results of the study revealed that the proposed approach could improve the classification of skateboarding tricks.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Muhammad Amirul, Abdullah
author_facet	Muhammad Amirul, Abdullah
author_sort	Muhammad Amirul, Abdullah
title	Classification of skateboarding tricks by synthesizing transfer learning models and machine learning classifiers using different input signal transformations
title_short	Classification of skateboarding tricks by synthesizing transfer learning models and machine learning classifiers using different input signal transformations
title_full	Classification of skateboarding tricks by synthesizing transfer learning models and machine learning classifiers using different input signal transformations
title_fullStr	Classification of skateboarding tricks by synthesizing transfer learning models and machine learning classifiers using different input signal transformations
title_full_unstemmed	Classification of skateboarding tricks by synthesizing transfer learning models and machine learning classifiers using different input signal transformations
title_sort	classification of skateboarding tricks by synthesizing transfer learning models and machine learning classifiers using different input signal transformations
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Manufacturing and Mechatronic Engineering Technology
publishDate	2022
url	http://umpir.ump.edu.my/id/eprint/35919/1/06.Classification%20of%20skateboarding%20tricks%20by%20synthesizing%20transfer%20learning%20models%20and%20machine%20learning%20classifiers.pdf
_version_	1783732249144852480

Classification of skateboarding tricks by synthesizing transfer learning models and machine learning classifiers using different input signal transformations

Similar Items