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Classification Analysis Of The Badminton Five Directional Lunges

Badminton lunge motion is important skill for players in order to have a fundamental footwork in badminton. Majority previous badminton studies on lunge motions investigated male players. The gap was that the findings reported were not applicable to the female players. There are no works conducted t...

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Main Author: Ho, Zhe Wei
Format: Monograph
Language:English
Published: Universiti Sains Malaysia 2018
Subjects:
T Technology
TJ Mechanical engineering and machinery
Online Access:http://eprints.usm.my/54126/1/Classification%20Analysis%20Of%20The%20Badminton%20Five%20Directional%20Lunges_Ho%20Zhe%20Wei_M4_2018.pdf
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spelling my.usm.eprints.54126 http://eprints.usm.my/54126/ Classification Analysis Of The Badminton Five Directional Lunges Ho, Zhe Wei T Technology TJ Mechanical engineering and machinery Badminton lunge motion is important skill for players in order to have a fundamental footwork in badminton. Majority previous badminton studies on lunge motions investigated male players. The gap was that the findings reported were not applicable to the female players. There are no works conducted to mine the patterns of directional badminton lunge motions. Therefore, this study attempted to (i) study the patterns of lunge motion in the badminton game, (ii) classify badminton players’ postures by lunge type and (iii) compare the differences in the badminton lunge patterns between university and national level players. The case study involved 11 university level and 2 national level players in badminton singles captures. Five directional lunge motions: center-forward, left-forward, right-forward, left-sideward and right-sideward lunge and its corresponding attributes were tracked through Kinovea software. Data mining concept is adopted in four stages: data pre-processing, data classification, significant attribute analysis and knowledge discovery using the WEKA software. REP Tree classifier is the best selected classifier for its strength and classification capability. The highest classification accuracy obtained for experimental data-USM and public data-SEA, were 93.75% and 93.01% respectively on REP Tree classifier. On selective attribute configuration, the identity (ID), game reaction time (GT) and type of lunge (LT) significantly enhanced the classification accuracy to 99.61% for experimental data-USM and 100% for the public data-SEA. Lunge type patterns were related to ID and GT. Conclusively, the identity, game reaction time and type of lunge were found being the key determinants for badminton lunge classification accounting for highest classification accuracy in REP Tree algorithm. Universiti Sains Malaysia 2018-06-01 Monograph NonPeerReviewed application/pdf en http://eprints.usm.my/54126/1/Classification%20Analysis%20Of%20The%20Badminton%20Five%20Directional%20Lunges_Ho%20Zhe%20Wei_M4_2018.pdf Ho, Zhe Wei (2018) Classification Analysis Of The Badminton Five Directional Lunges. Project Report. Universiti Sains Malaysia, Pusat Pengajian Kejuruteraan Mekanikal. (Submitted)
institution Universiti Sains Malaysia
building Hamzah Sendut Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sains Malaysia
content_source USM Institutional Repository
url_provider http://eprints.usm.my/
language English
topic T Technology
TJ Mechanical engineering and machinery
spellingShingle T Technology
TJ Mechanical engineering and machinery
Ho, Zhe Wei
Classification Analysis Of The Badminton Five Directional Lunges
description Badminton lunge motion is important skill for players in order to have a fundamental footwork in badminton. Majority previous badminton studies on lunge motions investigated male players. The gap was that the findings reported were not applicable to the female players. There are no works conducted to mine the patterns of directional badminton lunge motions. Therefore, this study attempted to (i) study the patterns of lunge motion in the badminton game, (ii) classify badminton players’ postures by lunge type and (iii) compare the differences in the badminton lunge patterns between university and national level players. The case study involved 11 university level and 2 national level players in badminton singles captures. Five directional lunge motions: center-forward, left-forward, right-forward, left-sideward and right-sideward lunge and its corresponding attributes were tracked through Kinovea software. Data mining concept is adopted in four stages: data pre-processing, data classification, significant attribute analysis and knowledge discovery using the WEKA software. REP Tree classifier is the best selected classifier for its strength and classification capability. The highest classification accuracy obtained for experimental data-USM and public data-SEA, were 93.75% and 93.01% respectively on REP Tree classifier. On selective attribute configuration, the identity (ID), game reaction time (GT) and type of lunge (LT) significantly enhanced the classification accuracy to 99.61% for experimental data-USM and 100% for the public data-SEA. Lunge type patterns were related to ID and GT. Conclusively, the identity, game reaction time and type of lunge were found being the key determinants for badminton lunge classification accounting for highest classification accuracy in REP Tree algorithm.
format Monograph
author Ho, Zhe Wei
author_facet Ho, Zhe Wei
author_sort Ho, Zhe Wei
title Classification Analysis Of The Badminton Five Directional Lunges
title_short Classification Analysis Of The Badminton Five Directional Lunges
title_full Classification Analysis Of The Badminton Five Directional Lunges
title_fullStr Classification Analysis Of The Badminton Five Directional Lunges
title_full_unstemmed Classification Analysis Of The Badminton Five Directional Lunges
title_sort classification analysis of the badminton five directional lunges
publisher Universiti Sains Malaysia
publishDate 2018
url http://eprints.usm.my/54126/1/Classification%20Analysis%20Of%20The%20Badminton%20Five%20Directional%20Lunges_Ho%20Zhe%20Wei_M4_2018.pdf
http://eprints.usm.my/54126/
_version_ 1743107788422250496
score 13.211869

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