The classification of skateboard trick manoeuvres through the integration of inertial measurement unit (imu) and machine learning

The growing interest in skateboarding as a competitive sport requires new motion analysis approaches and innovative ways to portray athletes' performance as previous classification of tricks techniques was often deemed inadequate in providing accurate evaluation during competition. Therefore, a...

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Main Author:	Muhammad Ar Rahim, Ibrahim
Format:	Thesis
Language:	English
Published:	2022
Subjects:	TA Engineering (General) Civil engineering (General) TS Manufactures
Online Access:	http://umpir.ump.edu.my/id/eprint/37630/1/ir.The%20classification%20of%20skateboard%20trick%20manoeuvres%20through%20the%20integration%20of%20inertial%20measurement%20unit%20%28imu%29%20and%20machine%20learning.pdf
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id	my-ump-ir.37630
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spelling	my-ump-ir.376302023-09-18T02:57:58Z The classification of skateboard trick manoeuvres through the integration of inertial measurement unit (imu) and machine learning 2022-06 Muhammad Ar Rahim, Ibrahim TA Engineering (General). Civil engineering (General) TS Manufactures The growing interest in skateboarding as a competitive sport requires new motion analysis approaches and innovative ways to portray athletes' performance as previous classification of tricks techniques was often deemed inadequate in providing accurate evaluation during competition. Therefore, an objective and fair means of evaluating skateboarding tricks were developed to analyze skateboarder’s tricks is non-trivial. This study aims at classifying flat ground tricks, namely Ollie, Kickflip, Pop Shove-it, Nollie Frontside Shove-it, and Frontside 180, through the use of Inertial Measurement Unit (IMU) and machine learning models. Six armature skateboarders executed five tricks for each type of trick repeatedly by five times. It is worth noting that the time-series (TS) domain input skateboard data were transformed to two different types of frequency domains, namely Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT). Therefore, both the time and frequency domain features were used to evaluate six machine learning models, Logistic Regression (LR), Random Forest (RF), k-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), Naïve Bayes (NB), and Support Vector Machine (SVM). In addition, two types of feature selection methods known as Wrapper and Embedded methods, were applied to identify the significant features. The datasets were split into 70:30 ratios for training and testing, respectively. It was shown from the study, that the RF-TS (All), RF-TS (Wrapper), RF-TS (Embedded), RF-DWT (All), RF-DWT (Wrapper), and RF-DWT (Embedded) yield 100% classification accuracy. Nevertheless, the RF-TS (Wrapper) is established to be the best as it utilises the least number of features (forty-one instead of fifty-four), which in turn reduces the complexity of the model for the classification of the tricks evaluated. Therefore, it is opined that the approach proposed can reasonably identify the tricks of the skateboard to help the judges evaluates the trick performances more precisely as opposed to the currently used subjective and traditional techniques. 2022-06 Thesis http://umpir.ump.edu.my/id/eprint/37630/ http://umpir.ump.edu.my/id/eprint/37630/1/ir.The%20classification%20of%20skateboard%20trick%20manoeuvres%20through%20the%20integration%20of%20inertial%20measurement%20unit%20%28imu%29%20and%20machine%20learning.pdf pdf en public masters Universiti Malaysia Pahang Faculty of Manufacturing and Mechatronic Engineering Technology Anwar P.P., Abdul Majeed
institution	Universiti Malaysia Pahang Al-Sultan Abdullah
collection	UMPSA Institutional Repository
language	English
advisor	Anwar P.P., Abdul Majeed
topic	TA Engineering (General) Civil engineering (General) TS Manufactures
spellingShingle	TA Engineering (General) Civil engineering (General) TS Manufactures Muhammad Ar Rahim, Ibrahim The classification of skateboard trick manoeuvres through the integration of inertial measurement unit (imu) and machine learning
description	The growing interest in skateboarding as a competitive sport requires new motion analysis approaches and innovative ways to portray athletes' performance as previous classification of tricks techniques was often deemed inadequate in providing accurate evaluation during competition. Therefore, an objective and fair means of evaluating skateboarding tricks were developed to analyze skateboarder’s tricks is non-trivial. This study aims at classifying flat ground tricks, namely Ollie, Kickflip, Pop Shove-it, Nollie Frontside Shove-it, and Frontside 180, through the use of Inertial Measurement Unit (IMU) and machine learning models. Six armature skateboarders executed five tricks for each type of trick repeatedly by five times. It is worth noting that the time-series (TS) domain input skateboard data were transformed to two different types of frequency domains, namely Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT). Therefore, both the time and frequency domain features were used to evaluate six machine learning models, Logistic Regression (LR), Random Forest (RF), k-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), Naïve Bayes (NB), and Support Vector Machine (SVM). In addition, two types of feature selection methods known as Wrapper and Embedded methods, were applied to identify the significant features. The datasets were split into 70:30 ratios for training and testing, respectively. It was shown from the study, that the RF-TS (All), RF-TS (Wrapper), RF-TS (Embedded), RF-DWT (All), RF-DWT (Wrapper), and RF-DWT (Embedded) yield 100% classification accuracy. Nevertheless, the RF-TS (Wrapper) is established to be the best as it utilises the least number of features (forty-one instead of fifty-four), which in turn reduces the complexity of the model for the classification of the tricks evaluated. Therefore, it is opined that the approach proposed can reasonably identify the tricks of the skateboard to help the judges evaluates the trick performances more precisely as opposed to the currently used subjective and traditional techniques.
format	Thesis
qualification_level	Master's degree
author	Muhammad Ar Rahim, Ibrahim
author_facet	Muhammad Ar Rahim, Ibrahim
author_sort	Muhammad Ar Rahim, Ibrahim
title	The classification of skateboard trick manoeuvres through the integration of inertial measurement unit (imu) and machine learning
title_short	The classification of skateboard trick manoeuvres through the integration of inertial measurement unit (imu) and machine learning
title_full	The classification of skateboard trick manoeuvres through the integration of inertial measurement unit (imu) and machine learning
title_fullStr	The classification of skateboard trick manoeuvres through the integration of inertial measurement unit (imu) and machine learning
title_full_unstemmed	The classification of skateboard trick manoeuvres through the integration of inertial measurement unit (imu) and machine learning
title_sort	classification of skateboard trick manoeuvres through the integration of inertial measurement unit (imu) and machine learning
granting_institution	Universiti Malaysia Pahang
granting_department	Faculty of Manufacturing and Mechatronic Engineering Technology
publishDate	2022
url	http://umpir.ump.edu.my/id/eprint/37630/1/ir.The%20classification%20of%20skateboard%20trick%20manoeuvres%20through%20the%20integration%20of%20inertial%20measurement%20unit%20%28imu%29%20and%20machine%20learning.pdf
_version_	1783732270075478016

The classification of skateboard trick manoeuvres through the integration of inertial measurement unit (imu) and machine learning

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