Sensor-based assessment using machine learning for predictive model of badminton skills

Badminton assessment is a process to evaluate the performance of players and it is very important for them to identify their strengths and weaknesses so as to improve their training effectiveness. Several conventional assessment methods, which are the lack of manpower, expertise and objective method...

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Main Author:	Chew, Zhen Shan
Format:	Thesis
Language:	English
Published:	2018
Subjects:	TK Electrical engineering Electronics Nuclear engineering
Online Access:	http://eprints.utm.my/id/eprint/79265/1/ChewZhenShanMFKE2018.pdf
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id	my-utm-ep.79265
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spelling	my-utm-ep.792652018-10-14T08:40:00Z Sensor-based assessment using machine learning for predictive model of badminton skills 2018 Chew, Zhen Shan TK Electrical engineering. Electronics Nuclear engineering Badminton assessment is a process to evaluate the performance of players and it is very important for them to identify their strengths and weaknesses so as to improve their training effectiveness. Several conventional assessment methods, which are the lack of manpower, expertise and objective methods. Besides, standard parameters and assessment model using machine learning for badminton assessment are still at research level. The main objective of this research is to design and develop a novel and effective system for badminton assessment . In this thesis, a total of three assessment modules (Module 1: Badminton Serving Accuracy, Module 2: Badminton Shots Quality, Module 3: Player’s Agility) were developed to extract the required measurable parameters of players through their serves, hits and agility. A 9 degree of freedom wireless sensor, an APDM Opal sensor and a badminton feedback sensor, XiaoYu 2.0 were used in this study to collect kinematic parameters such as acceleration , power and rotational speed. All the three modules were tested with 3 strong and 6 normal players and there were totally 46 collected features. A total of 39 out of 46 features have been proved being significantly different using t-test method. The three feature selection methods were named Relief, Principal Component Analysis and Correlation Feature Selection and were used for feature extraction. Then, the acquired datasets were tested by seven machine learning models , namely Random Tree (RT), Random Forest, Artificial Neural Network, K Star, Multiple Linear Regression, Gaussian Process and Support Vector Machine. Total of 21 assessment models had been constructed. The results show that the RT model produces prediction accuracy of 90.84% and correlation value of r=0.86. 2018 Thesis http://eprints.utm.my/id/eprint/79265/ http://eprints.utm.my/id/eprint/79265/1/ChewZhenShanMFKE2018.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Electrical Engineering Faculty of Electrical Engineering
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	TK Electrical engineering Electronics Nuclear engineering
spellingShingle	TK Electrical engineering Electronics Nuclear engineering Chew, Zhen Shan Sensor-based assessment using machine learning for predictive model of badminton skills
description	Badminton assessment is a process to evaluate the performance of players and it is very important for them to identify their strengths and weaknesses so as to improve their training effectiveness. Several conventional assessment methods, which are the lack of manpower, expertise and objective methods. Besides, standard parameters and assessment model using machine learning for badminton assessment are still at research level. The main objective of this research is to design and develop a novel and effective system for badminton assessment . In this thesis, a total of three assessment modules (Module 1: Badminton Serving Accuracy, Module 2: Badminton Shots Quality, Module 3: Player’s Agility) were developed to extract the required measurable parameters of players through their serves, hits and agility. A 9 degree of freedom wireless sensor, an APDM Opal sensor and a badminton feedback sensor, XiaoYu 2.0 were used in this study to collect kinematic parameters such as acceleration , power and rotational speed. All the three modules were tested with 3 strong and 6 normal players and there were totally 46 collected features. A total of 39 out of 46 features have been proved being significantly different using t-test method. The three feature selection methods were named Relief, Principal Component Analysis and Correlation Feature Selection and were used for feature extraction. Then, the acquired datasets were tested by seven machine learning models , namely Random Tree (RT), Random Forest, Artificial Neural Network, K Star, Multiple Linear Regression, Gaussian Process and Support Vector Machine. Total of 21 assessment models had been constructed. The results show that the RT model produces prediction accuracy of 90.84% and correlation value of r=0.86.
format	Thesis
qualification_level	Master's degree
author	Chew, Zhen Shan
author_facet	Chew, Zhen Shan
author_sort	Chew, Zhen Shan
title	Sensor-based assessment using machine learning for predictive model of badminton skills
title_short	Sensor-based assessment using machine learning for predictive model of badminton skills
title_full	Sensor-based assessment using machine learning for predictive model of badminton skills
title_fullStr	Sensor-based assessment using machine learning for predictive model of badminton skills
title_full_unstemmed	Sensor-based assessment using machine learning for predictive model of badminton skills
title_sort	sensor-based assessment using machine learning for predictive model of badminton skills
granting_institution	Universiti Teknologi Malaysia, Faculty of Electrical Engineering
granting_department	Faculty of Electrical Engineering
publishDate	2018
url	http://eprints.utm.my/id/eprint/79265/1/ChewZhenShanMFKE2018.pdf
_version_	1747818186453024768

Sensor-based assessment using machine learning for predictive model of badminton skills

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