The classification of taekwondo kicks via machine learning: A feature selection investigation
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...
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: | |
Online Access: | http://umpir.ump.edu.my/id/eprint/33668/1/The%20classification%20of%20taekwondo%20kicks%20via%20machine%20learning.pdf |
Similar Items
-
The classification of elbow extension and flexion: A feature selection investigation
by: Mohamad Ilyas, Rizan, et al.
Published: (2020) -
The classification of skateboarding tricks by means of support vector machine: An evaluation of significant time-domain features
by: Muhammad Amirul, Abdullah, et al.
Published: (2020) -
The classification of skateboarding tricks: A support vector machine hyperparameter evaluation optimisation
by: Muhammad Ar Rahim, Ibrahim, et al.
Published: (2021) -
The Classification of Skateboarding Tricks by Means of the Integration of Transfer Learning and Machine Learning Models
by: Muhammad Nur Aiman, Shapiee, et al.
Published: (2020) -
The classification of skateboarding trick manoeuvres through the integration of IMU and machine learning
by: Muhammad Amirul, Abdullah, et al.
Published: (2020)