Classification of Sepak Takraw Kicks Using Machine Learning

Fu Yang Tan, Mohd Hasnun Arif Hassan*, Anwar P. P. Abdul Majeed, Mohd Azraai Mohd Razman, Muhammad Amirul Abdullah

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

2 Citations (Scopus)

Abstract

Sepak Takraw has gained popularity over the years. Kinematics of the leg determine the quality and accuracy of the kick. However, the classification of Sepak Takraw kick using machine learning has never been explored. This study aims to classify the most typical kicks in Sepak Takraw namely the serve (or known as tekong), feeder and spike based on the leg’s kinematics using machine learning. Collegiate sepak takraw players participated in the data collection. The participants wore the inertial measurement unit sensor on their shank while performing the kicks. From the kinematics data recorded, several mathematical features were extracted and calculated. Machine learning algorithms such as the k-nearest neighbors (kNN), support vector machine (SVM), artificial neural networks (ANN), naive bayes (NB), random forest (RF), and logistic regression (LR) were applied to classify the types of kicks performed using fivefold cross-validation technique with 70% train data and 30% test data. It was found that ANN predicts all the test data correctly with 100% accuracy, followed by NB, SVM, RF and LR with 1 misclassification at 96.3% accuracy, kNN has the lowest prediction accuracy at 77.78%. This study shows that machine learning model is capable of classifying sepak takraw kicks. This can be used in training young athletes to ensure they perform the kicks properly, with correct skills.

Original languageEnglish
Title of host publicationHuman-Centered Technology for a Better Tomorrow - Proceedings of HUMENS 2021
EditorsMohd Hasnun Hassan, Zulkifli Ahmad (a) Manap, Mohamad Zairi Baharom, Nasrul Hadi Johari, Ummu Kulthum Jamaludin, Muhammad Hilmi Jalil, Idris Mat Sahat, Mohd Nadzeri Omar
PublisherSpringer Science and Business Media Deutschland GmbH
Pages321-331
Number of pages11
ISBN (Print)9789811641145
DOIs
Publication statusPublished - 2022
Externally publishedYes
EventHuman Engineering Symposium, HUMENS 2021 - Virtual, Online
Duration: 22 Feb 202122 Feb 2021

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Conference

ConferenceHuman Engineering Symposium, HUMENS 2021
CityVirtual, Online
Period22/02/2122/02/21

Keywords

  • Classification
  • Leg’s kinematics
  • Machine learning
  • Sepak takraw kicks

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