Muhammad Syafi’i, Mass Duki and Muhammad Nur Aiman, Shapiee and Muhammad Amirul, Abdullah and Ismail, Mohd Khairuddin and Mohd Azraai, Mohd Razman and Anwar P. P., Abdul Majeed (2021) The classification of taekwondo kicks via machine learning: A feature selection investigation. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 3 (1). pp. 61-67. ISSN 2637-0883. (Published)
|
Pdf
The classification of taekwondo kicks via machine learning.pdf Available under License Creative Commons Attribution Non-commercial. Download (433kB) | Preview |
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%.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Taekwondo; Supervised learning; Classifier; Machine learning; Martial art |
| Subjects: | T Technology > TJ Mechanical engineering and machinery |
| Faculty/Division: | Institute of Postgraduate Studies Faculty of Manufacturing and Mechatronic Engineering Technology |
| Depositing User: | Mrs Norsaini Abdul Samat |
| Date Deposited: | 11 Apr 2022 02:47 |
| Last Modified: | 11 Apr 2022 02:47 |
| URI: | http://umpir.ump.edu.my/id/eprint/33668 |
| Download Statistic: | View Download Statistics |
Actions (login required)
 |
View Item |