TY - GEN
T1 - The Classification of Skateboarding Tricks
T2 - Innovative Manufacturing, Mechatronics and Materials Forum, iM3F 2020
AU - Ibrahim, Muhammad Ar Rahim
AU - Shapiee, Muhammad Nur Aiman
AU - Abdullah, Muhammad Amirul
AU - Razman, Mohd Azraai Mohd
AU - Musa, Rabiu Muazu
AU - Majeed, Anwar P.P.Abdul
N1 - Funding Information:
Acknowledgements 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:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2022
Y1 - 2022
N2 - The growing interest in skateboarding as a competitive sport requires new motion analysis approaches and innovative ways to portray athletes’ results as the conventional technique of the classification of the tricks is often inadequate in providing accurate and often biased evaluation during competition. This paper aims to identify the suitable hyperparameters of a Support Vector Machine (SVM) classifier in classifying five different skateboarding tricks (Ollie, Kickflip, Frontside 180, Pop Shove-it, and Nollie Frontside Shove-it) based on frequency-domain features extracted from Inertial Measurement Unit (IMU). An amateur skateboarder with the age of 23 years old performed five different skateboard tricks and repeated for five times. The signals obtained then were converted from time-domain to frequency-domain through Fast Fourier Transform (FFT), and a number of features (mean, kurtosis, skewness, standard deviation, root mean square and peak-to-peak corresponding to x–y–z axis of IMU reading) were extracted from the frequency dataset. Different hyperparameters of the SVM model were optimised via grid search sweep. It was found that a sigmoid kernel with 0.01 of gamma and regularisation, C value of 10 were found to be the optimum hyperparameters as it could attain a classification accuracy of 100%. The present findings imply that the proposed approach can well identify the tricks to assist the judges in providing a more objective-based evaluation.
AB - The growing interest in skateboarding as a competitive sport requires new motion analysis approaches and innovative ways to portray athletes’ results as the conventional technique of the classification of the tricks is often inadequate in providing accurate and often biased evaluation during competition. This paper aims to identify the suitable hyperparameters of a Support Vector Machine (SVM) classifier in classifying five different skateboarding tricks (Ollie, Kickflip, Frontside 180, Pop Shove-it, and Nollie Frontside Shove-it) based on frequency-domain features extracted from Inertial Measurement Unit (IMU). An amateur skateboarder with the age of 23 years old performed five different skateboard tricks and repeated for five times. The signals obtained then were converted from time-domain to frequency-domain through Fast Fourier Transform (FFT), and a number of features (mean, kurtosis, skewness, standard deviation, root mean square and peak-to-peak corresponding to x–y–z axis of IMU reading) were extracted from the frequency dataset. Different hyperparameters of the SVM model were optimised via grid search sweep. It was found that a sigmoid kernel with 0.01 of gamma and regularisation, C value of 10 were found to be the optimum hyperparameters as it could attain a classification accuracy of 100%. The present findings imply that the proposed approach can well identify the tricks to assist the judges in providing a more objective-based evaluation.
KW - Classification
KW - IMU sensor
KW - Machine learning
KW - Skateboard
KW - Trick
UR - http://www.scopus.com/inward/record.url?scp=85112545127&partnerID=8YFLogxK
U2 - 10.1007/978-981-33-4597-3_93
DO - 10.1007/978-981-33-4597-3_93
M3 - Conference Proceeding
AN - SCOPUS:85112545127
SN - 9789813345966
T3 - Lecture Notes in Electrical Engineering
SP - 1013
EP - 1022
BT - Recent Trends in Mechatronics Towards Industry 4.0 - Selected Articles from iM3F 2020
A2 - Ab. Nasir, Ahmad Fakhri
A2 - Ibrahim, Ahmad Najmuddin
A2 - Ishak, Ismayuzri
A2 - Mat Yahya, Nafrizuan
A2 - Zakaria, Muhammad Aizzat
A2 - P. P. Abdul Majeed, Anwar
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 August 2020 through 6 August 2020
ER -