The classification of skateboarding tricks by means of support vector machine: An evaluation of significant time-domain features

• Main Authors: 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
• Format: Book Section
• Language: English
• Published: Springer 2020
• Subjects: T Technology (General)
• Online Access: 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; http://umpir.ump.edu.my/id/eprint/32613/; https://doi.org/10.1007/978-981-15-6025-5_12; https://doi.org/10.1007/978-981-15-6025-5_12
• Institution: Universiti Malaysia Pahang

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 ±...