The Classification of Skateboarding Trick Manoeuvres: A Frequency-Domain Evaluation

Muhammad Ar Rahim Ibrahim*, Muhammad Nur Aiman Shapiee, Muhammad Amirul Abdullah, Mohd Azraai Mohd Razman, Rabiu Muazu Musa, Anwar P.P. Abdul Majeed

*Corresponding author for this work

Research output: Chapter in Book or Report/Conference proceedingConference Proceedingpeer-review

2 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationEmbracing Industry 4.0 - Selected Articles from MUCET 2019
EditorsMohd Azraai Mohd Razman, Jessnor Arif Mat Jizat, Nafrizuan Mat Yahya, Hyun Myung, Amar Faiz Zainal Abidin, Mohamad Shaiful Abdul Karim
Number of pages12
ISBN (Print)9789811560248
Publication statusPublished - 2020
Externally publishedYes
Event11th Malaysian Technical Universities Conference on Engineering and Technology, MUCET 2019 - Kuantan, Malaysia
Duration: 19 Nov 201922 Nov 2019

Publication series

NameLecture Notes in Electrical Engineering
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119


Conference11th Malaysian Technical Universities Conference on Engineering and Technology, MUCET 2019


  • Classification
  • Fast fourier transform
  • Feature selection
  • Frequency-domain
  • IMU
  • Machine learning
  • Skateboarding tricks


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