The classification of taekwondo kicks via machine learning: A feature selection investigation

• Main Authors: Muhammad Syafi’i, Mass Duki, Muhammad Nur Aiman, Shapiee, Muhammad Amirul, Abdullah, Ismail, Mohd Khairuddin, Mohd Azraai, Mohd Razman, Anwar P. P., Abdul Majeed
• Format: Article
• Language: English
• Published: Penerbit UMP 2021
• Subjects: TJ Mechanical engineering and machinery
• Online Access: http://umpir.ump.edu.my/id/eprint/33668/1/The%20classification%20of%20taekwondo%20kicks%20via%20machine%20learning.pdf; http://umpir.ump.edu.my/id/eprint/33668/; https://doi.org/10.15282/mekatronika.v3i1.7153; https://doi.org/10.15282/mekatronika.v3i1.7153

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...