TY - GEN
T1 - The Classification of Skateboarding Tricks by Means of Support Vector Machine
T2 - 11th Malaysian Technical Universities Conference on Engineering and Technology, MUCET 2019
AU - Abdullah, Muhammad Amirul
AU - Ibrahim, Muhammad Ar Rahim
AU - Shapiee, Muhammad Nur Aiman
AU - Abdul Majeed, Anwar P.P.
AU - Mohd Razman, Mohd Azraai
AU - Musa, Rabiu Muazu
AU - Zakaria, Muhammad Aizzat
N1 - Funding Information:
The authors would like to acknowledge the Ministry of Education, Malaysia and Universiti Malaysia Pahang for supporting and funding this research via FRGS/1/2019/TK03/UMP/02/6 (RDU1901115).
Publisher Copyright:
© 2020, Springer Nature Singapore Pte Ltd.
PY - 2020
Y1 - 2020
N2 - This study aims to improve classification accuracy of different Support Vector Machine (SVM) models in classifying flat ground tricks namely Ollie, Kickflip, 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.
AB - This study aims to improve classification accuracy of different Support Vector Machine (SVM) models in classifying flat ground tricks namely Ollie, Kickflip, 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.
KW - Classification
KW - Feature selection
KW - Machine learning
KW - Skateboarding
UR - http://www.scopus.com/inward/record.url?scp=85088575011&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-6025-5_12
DO - 10.1007/978-981-15-6025-5_12
M3 - Conference Proceeding
AN - SCOPUS:85088575011
SN - 9789811560248
T3 - Lecture Notes in Electrical Engineering
SP - 125
EP - 132
BT - Embracing Industry 4.0 - Selected Articles from MUCET 2019
A2 - Mohd Razman, Mohd Azraai
A2 - Mat Jizat, Jessnor Arif
A2 - Mat Yahya, Nafrizuan
A2 - Myung, Hyun
A2 - Zainal Abidin, Amar Faiz
A2 - Abdul Karim, Mohamad Shaiful
PB - Springer
Y2 - 19 November 2019 through 22 November 2019
ER -