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: | , , , , , |
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| Format: | Article |
| Language: | English |
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Penerbit UMP
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/33668/1/The%20classification%20of%20taekwondo%20kicks%20via%20machine%20learning.pdf |
| _version_ | 1825814252071419904 |
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| author | Muhammad Syafi’i, Mass Duki Muhammad Nur Aiman, Shapiee Muhammad Amirul, Abdullah Ismail, Mohd Khairuddin Mohd Azraai, Mohd Razman Anwar P. P., Abdul Majeed |
| author_facet | Muhammad Syafi’i, Mass Duki Muhammad Nur Aiman, Shapiee Muhammad Amirul, Abdullah Ismail, Mohd Khairuddin Mohd Azraai, Mohd Razman Anwar P. P., Abdul Majeed |
| author_sort | Muhammad Syafi’i, Mass Duki |
| collection | UMP |
| description | 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%. |
| first_indexed | 2024-03-06T12:56:01Z |
| format | Article |
| id | UMPir33668 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:56:01Z |
| publishDate | 2021 |
| publisher | Penerbit UMP |
| record_format | dspace |
| spelling | UMPir336682022-04-11T02:47:52Z http://umpir.ump.edu.my/id/eprint/33668/ 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 TJ Mechanical engineering and machinery 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%. Penerbit UMP 2021 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/33668/1/The%20classification%20of%20taekwondo%20kicks%20via%20machine%20learning.pdf 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) https://doi.org/10.15282/mekatronika.v3i1.7153 https://doi.org/10.15282/mekatronika.v3i1.7153 |
| spellingShingle | TJ Mechanical engineering and machinery Muhammad Syafi’i, Mass Duki Muhammad Nur Aiman, Shapiee Muhammad Amirul, Abdullah Ismail, Mohd Khairuddin Mohd Azraai, Mohd Razman Anwar P. P., Abdul Majeed The classification of taekwondo kicks via machine learning: A feature selection investigation |
| title | The classification of taekwondo kicks via machine learning: A feature selection investigation |
| title_full | The classification of taekwondo kicks via machine learning: A feature selection investigation |
| title_fullStr | The classification of taekwondo kicks via machine learning: A feature selection investigation |
| title_full_unstemmed | The classification of taekwondo kicks via machine learning: A feature selection investigation |
| title_short | The classification of taekwondo kicks via machine learning: A feature selection investigation |
| title_sort | classification of taekwondo kicks via machine learning a feature selection investigation |
| topic | TJ Mechanical engineering and machinery |
| url | http://umpir.ump.edu.my/id/eprint/33668/1/The%20classification%20of%20taekwondo%20kicks%20via%20machine%20learning.pdf |
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