The classification of skateboarding tricks via transfer learning pipelines

Abdullah, Muhammad Amirul and Ibrahim, Muhammad Ar Rahim and Shapiee, Muhammad Nur Aiman and Zakaria, Muhammad Aizzat and Razman, Mohd Azraai Mohd and Musa, Rabiu Muazu and Abu Osman, Noor Azuan and Majeed, Anwar P. P. Abdul (2021) The classification of skateboarding tricks via transfer learning pipelines. PeerJ Computer Science, 7. ISSN 2376-5992, DOI

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This study aims at classifying flat ground tricks, namely Ollie, Kickflip, Shove-it, Nollie and Frontside 180, through the identification of significant input image transformation on different transfer learning models with optimized Support Vector Machine (SVM) classifier. A total of six amateur skateboarders (20 +/- 7 years of age with at least 5.0 years of 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 six raw signals extracted. A total of two input image type, namely raw data (RAW) and Continous Wavelet Transform (CWT), as well as six transfer learning models from three different families along with grid-searched optimized SVM, were investigated towards its efficacy in classifying the skateboarding tricks. It was shown from the study that RAW and CWT input images on MobileNet, MobileNetV2 and ResNet101 transfer learning models demonstrated the best test accuracy at 100% on the test dataset. Nonetheless, by evaluating the computational time amongst the best models, it was established that the CWT-MobileNet-Optimized SVM pipeline was found to be the best. It could be concluded that the proposed method is able to facilitate the judges as well as coaches in identifying skateboarding tricks execution.

Item Type: Article
Funders: Ministry of Education, Malaysia through the Fundamental Research Grant Scheme [Grant No: FRGS/1/2019/TK03/UMP/02/6], Universiti Malaysia Pahang [Grant No: RDU1901115]
Uncontrolled Keywords: Classification; Support vector machine; Skateboarding; Machine learning; Transfer learning
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Engineering
Depositing User: Ms Zaharah Ramly
Date Deposited: 17 Jun 2022 08:10
Last Modified: 17 Jun 2022 08:10

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