The Classification of Taekwondo Kicks Via Machine Learning: A feature selection investigation

Authors

  • Muhammad Syafi’i Mass Duki Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia.
  • Muhammad Nur Aiman Shapiee Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia.
  • Muhammad Amirul Abdullah Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia.
  • Ismail Mohd Khairuddin Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia.
  • Mohd Azraai Mohd Razman Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia.
  • Anwar P. P. Abdul Majeed Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, 26600 Pekan Pahang, Malaysia.

DOI:

https://doi.org/10.15282/mekatronika.v3i1.7153

Keywords:

Taekwondo, Supervised learning, Classifier, Machine learning, Martial art

Abstract

Martial art strike classification by machine learning has drawn more attention over the past decade. The unique signal from each technique makes it harder to be recognized. Thus, this paper proposed an SVM, Random Forest, k-NN, and Naïve Bayes classification method applied to the time-domain signal to classify the three type of taekwondo technique. Data collected from the total of five participant and statistical features such as mean, median, minimum, maximum, standard deviation, variance, skewness, kurtosis, and standard error mean were extracted from the signal. After that, the data will be trained using selected rank features and hold-out method with k-fold cross-validation applied to the training and testing data. Therefore, with ANOVA test as features selection and 60:40 ratio of a hold-out method, Random Forest classifier score the highest accuracy of 86.7%..

Downloads

Published

2021-06-20

How to Cite

[1]
M. S. . Mass Duki, M. N. A. Shapiee, M. A. Abdullah, I. M. Khairuddin, M. A. Mohd Razman, and A. P. P. Abdul Majeed, “The Classification of Taekwondo Kicks Via Machine Learning: A feature selection investigation”, MEKATRONIKA, vol. 3, no. 1, pp. 61–67, Jun. 2021.

Issue

Section

Original Article

Most read articles by the same author(s)

<< < 1 2 3