The classification of skateboarding tricks by means of support vector machine: An evaluation of significant time-domain features
This study aims to improve classification accuracy of different Support Vector Machine (SVM) models in classifying flat ground tricks namely Ollie, Kick-flip, Shove-it, Nollie and Frontside 180 through the identification of significant time-domain features. An amateur skateboarder (23 years of age ±...
| Main Authors: | , , , , , , |
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| Format: | Book Chapter |
| Language: | English |
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Springer
2020
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/32613/ http://umpir.ump.edu.my/id/eprint/32613/1/CONFERENCE%20%282%29%20-%20The%20classification%20of%20skateboarding%20tricks%20by%20means%20of%20support%20vector%20machine%20an%20evaluation%20of%20significant%20time-domain%20features.pdf |
| _version_ | 1848824065731592192 |
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| author | Muhammad Amirul, Abdullah Muhammad Ar Rahim, Ibrahim Muhammad Nur Aiman, Shapiee Anwar P. P, Abdul Majeed Mohd Azraai, Mohd Razman, Rabiu Muazu, Musa Muhammad Aizzat, Zakaria |
| author_facet | Muhammad Amirul, Abdullah Muhammad Ar Rahim, Ibrahim Muhammad Nur Aiman, Shapiee Anwar P. P, Abdul Majeed Mohd Azraai, Mohd Razman, Rabiu Muazu, Musa Muhammad Aizzat, Zakaria |
| author_sort | Muhammad Amirul, Abdullah |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | This study aims to improve classification accuracy of different Support Vector Machine (SVM) models in classifying flat ground tricks namely Ollie, Kick-flip, Shove-it, Nollie and Frontside 180 through the identification of significant time-domain features. An amateur skateboarder (23 years of age ±5.0 years’ experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (IMU sensor fused) on a cemented ground. From the IMU data a total of 36 features were extracted through statistical measures. The significant features were identified through two feature selection methods, namely Pearson and Chi-Squared. The variation of the SVM models (kernel-based) was evaluated both on all features and selected features in classifying the skateboarding tricks. It was shown from the study that all classifiers improved significantly in terms of training accuracy, prediction speed, training time and test accuracy. The Cubic-based SVM and Quadratic-based SVM demonstrated a 100% accuracy on both the test and train dataset, however, the Cubic-based SVM model provided the fastest training time and prediction speed between the two models. It could be concluded that the proposed method is able to improve the classification of the skateboarding tricks well. |
| first_indexed | 2025-11-15T03:07:06Z |
| format | Book Chapter |
| id | ump-32613 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:07:06Z |
| publishDate | 2020 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-326132021-11-17T08:19:51Z http://umpir.ump.edu.my/id/eprint/32613/ The classification of skateboarding tricks by means of support vector machine: An evaluation of significant time-domain features Muhammad Amirul, Abdullah Muhammad Ar Rahim, Ibrahim Muhammad Nur Aiman, Shapiee Anwar P. P, Abdul Majeed Mohd Azraai, Mohd Razman, Rabiu Muazu, Musa Muhammad Aizzat, Zakaria T Technology (General) This study aims to improve classification accuracy of different Support Vector Machine (SVM) models in classifying flat ground tricks namely Ollie, Kick-flip, Shove-it, Nollie and Frontside 180 through the identification of significant time-domain features. An amateur skateboarder (23 years of age ±5.0 years’ experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (IMU sensor fused) on a cemented ground. From the IMU data a total of 36 features were extracted through statistical measures. The significant features were identified through two feature selection methods, namely Pearson and Chi-Squared. The variation of the SVM models (kernel-based) was evaluated both on all features and selected features in classifying the skateboarding tricks. It was shown from the study that all classifiers improved significantly in terms of training accuracy, prediction speed, training time and test accuracy. The Cubic-based SVM and Quadratic-based SVM demonstrated a 100% accuracy on both the test and train dataset, however, the Cubic-based SVM model provided the fastest training time and prediction speed between the two models. It could be concluded that the proposed method is able to improve the classification of the skateboarding tricks well. Springer 2020-07-09 Book Chapter PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/32613/1/CONFERENCE%20%282%29%20-%20The%20classification%20of%20skateboarding%20tricks%20by%20means%20of%20support%20vector%20machine%20an%20evaluation%20of%20significant%20time-domain%20features.pdf Muhammad Amirul, Abdullah and Muhammad Ar Rahim, Ibrahim and Muhammad Nur Aiman, Shapiee and Anwar P. P, Abdul Majeed and Mohd Azraai, Mohd Razman, and Rabiu Muazu, Musa and Muhammad Aizzat, Zakaria (2020) The classification of skateboarding tricks by means of support vector machine: An evaluation of significant time-domain features. In: Embracing Industry 4.0. Lecture Notes in Electrical Engineering, 678 (11). Springer, Malaysia, pp. 125-132. ISBN 978-981-15-6025-5 https://doi.org/10.1007/978-981-15-6025-5_12 https://doi.org/10.1007/978-981-15-6025-5_12 |
| spellingShingle | T Technology (General) Muhammad Amirul, Abdullah Muhammad Ar Rahim, Ibrahim Muhammad Nur Aiman, Shapiee Anwar P. P, Abdul Majeed Mohd Azraai, Mohd Razman, Rabiu Muazu, Musa Muhammad Aizzat, Zakaria The classification of skateboarding tricks by means of support vector machine: An evaluation of significant time-domain features |
| title | The classification of skateboarding tricks by means of support vector machine: An evaluation of significant time-domain features |
| title_full | The classification of skateboarding tricks by means of support vector machine: An evaluation of significant time-domain features |
| title_fullStr | The classification of skateboarding tricks by means of support vector machine: An evaluation of significant time-domain features |
| title_full_unstemmed | The classification of skateboarding tricks by means of support vector machine: An evaluation of significant time-domain features |
| title_short | The classification of skateboarding tricks by means of support vector machine: An evaluation of significant time-domain features |
| title_sort | classification of skateboarding tricks by means of support vector machine: an evaluation of significant time-domain features |
| topic | T Technology (General) |
| url | http://umpir.ump.edu.my/id/eprint/32613/ http://umpir.ump.edu.my/id/eprint/32613/ http://umpir.ump.edu.my/id/eprint/32613/ http://umpir.ump.edu.my/id/eprint/32613/1/CONFERENCE%20%282%29%20-%20The%20classification%20of%20skateboarding%20tricks%20by%20means%20of%20support%20vector%20machine%20an%20evaluation%20of%20significant%20time-domain%20features.pdf |