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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
http://eprints.utm.my/id/eprint/79265/
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spelling my.utm.792652018-10-14T08:40:00Z http://eprints.utm.my/id/eprint/79265/ Sensor-based assessment using machine learning for predictive model of badminton skills 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 NonPeerReviewed application/pdf en http://eprints.utm.my/id/eprint/79265/1/ChewZhenShanMFKE2018.pdf Chew, Zhen Shan (2018) Sensor-based assessment using machine learning for predictive model of badminton skills. Masters thesis, Universiti Teknologi Malaysia, Faculty of Electrical Engineering.
institution Universiti Teknologi Malaysia
building UTM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Malaysia
content_source UTM Institutional Repository
url_provider http://eprints.utm.my/
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
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
publishDate 2018
url http://eprints.utm.my/id/eprint/79265/1/ChewZhenShanMFKE2018.pdf
http://eprints.utm.my/id/eprint/79265/
_version_ 1643658145536409600
score 13.211869

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