TY - GEN
T1 - The Classification of Skateboarding Trick Manoeuvres
T2 - 11th Malaysian Technical Universities Conference on Engineering and Technology, MUCET 2019
AU - Ibrahim, Muhammad Ar Rahim
AU - Shapiee, Muhammad Nur Aiman
AU - Abdullah, Muhammad Amirul
AU - Razman, Mohd Azraai Mohd
AU - Musa, Rabiu Muazu
AU - Abdul Majeed, Anwar P.P.
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 - The growing interest in skateboarding as a competitive sport requires new motion analysis approaches and innovative ways to portray athletes’ results as previous techniques in the identification of the tricks was often inadequate in providing accurate evaluation during competition. Therefore, there is a need to introduce an unprejudiced method of evaluation in skateboarding competitions. This paper presents the classification of five different skateboarding tricks (Ollie, Kickflip, Frontside 180, Pop Shove-it, and Nollie Frontside Shove-it) through the identification os significant frequency-domain signals collected via Inertial Measurement Unit (IMU) and the use of machine learning models. One male skateboarder (age: 23 years old) performed five different tricks repeatedly for several times. The time-domain data acquired from the IMU were converted to frequency-domain by employing Fast Fourier Transform (FFT) and a number of statistical features (mean, kurtosis, skewness, standard deviation, root mean square and peak-to-peak corresponding to x-y-z-axis of the IMU) were then extracted. Significant features were then identified from the Information Gain (IG) scoring. It was shown from the study that the Naïve Bayes (NB) classifier is able to acquire the highest classification accuracy of 100% on the test data compared to the other evaluated classifiers, namely Artificial Neural Network (ANN) and Support Vector Machine (SVM), by utilising the selected features, suggesting that the proposed methodology could provide an objective-based evaluation of the tricks.
AB - The growing interest in skateboarding as a competitive sport requires new motion analysis approaches and innovative ways to portray athletes’ results as previous techniques in the identification of the tricks was often inadequate in providing accurate evaluation during competition. Therefore, there is a need to introduce an unprejudiced method of evaluation in skateboarding competitions. This paper presents the classification of five different skateboarding tricks (Ollie, Kickflip, Frontside 180, Pop Shove-it, and Nollie Frontside Shove-it) through the identification os significant frequency-domain signals collected via Inertial Measurement Unit (IMU) and the use of machine learning models. One male skateboarder (age: 23 years old) performed five different tricks repeatedly for several times. The time-domain data acquired from the IMU were converted to frequency-domain by employing Fast Fourier Transform (FFT) and a number of statistical features (mean, kurtosis, skewness, standard deviation, root mean square and peak-to-peak corresponding to x-y-z-axis of the IMU) were then extracted. Significant features were then identified from the Information Gain (IG) scoring. It was shown from the study that the Naïve Bayes (NB) classifier is able to acquire the highest classification accuracy of 100% on the test data compared to the other evaluated classifiers, namely Artificial Neural Network (ANN) and Support Vector Machine (SVM), by utilising the selected features, suggesting that the proposed methodology could provide an objective-based evaluation of the tricks.
KW - Classification
KW - Fast fourier transform
KW - Feature selection
KW - Frequency-domain
KW - IMU
KW - Machine learning
KW - Skateboarding tricks
UR - http://www.scopus.com/inward/record.url?scp=85088572653&partnerID=8YFLogxK
U2 - 10.1007/978-981-15-6025-5_17
DO - 10.1007/978-981-15-6025-5_17
M3 - Conference Proceeding
AN - SCOPUS:85088572653
SN - 9789811560248
T3 - Lecture Notes in Electrical Engineering
SP - 183
EP - 194
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 -
