The Effect of Image Input Transformation from Inertial Measurement Unit Data on the Classification of Skateboarding Tricks - Muhammad Amirul, Abdullah; Muhammad Ar Rahim, Ibrahim; Muhammad Nur Aiman, Shapiee; Muhammad Aizzat, Zakaria; Mohd Azraai, Mohd Razman; Musa, Rabiu Muazu; Anwar P. P., Abdul Majeed (Springer, 2021)
This study aims to improve the classification of 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. Six goofy skateboarders (23 years of age ± 5.0 years’ experience) executed five tricks for each type of trick repeatedly on a customized ORY skateboard (Inertial Measurement Unit (IMU) sensor fused) on a cemented ground. From the IMU data, six raw signals were extracted. The best input image transformation and transfer learning model were identified through two input image transformations synthesized, namely raw data (RAW) and Fast Fourier Transform (FFT), and six transfer learning models based on default arguments from the Keras library. The variation of the SVM models (via different hyperparameters) was evaluated both on input image transformation and on transfer learning model in classifying the skateboarding tricks. It was shown from the study that RAW input image on three transfer learning models (DenseNet121, InceptionResNetV2, and ResNet101) demonstrated the 100% accuracy on all train, train and validation dataset. It could be concluded that the proposed method is able to improve the classification of the skateboarding tricks well.