The classification of taekwondo kicks via machine learning: A feature selection investigation - Muhammad Syafi’i, Mass Duki; Muhammad Nur Aiman, Shapiee; Muhammad Amirul, Abdullah; Ismail, Mohd Khairuddin; Mohd Azraai, Mohd Razman; Anwar P. P., Abdul Majeed (Penerbit UMP, 2021)
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%.