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.
Uric acid is a waste product of the human body where high levels of it or hyperuricemia can lead to gout, kidney disease and other health issues. In this paper, Finite Difference Time Doman (FDTD) simulation method was used to develop a plasmonic optical sensor to detect uric acid with molarity ranging from 0 to 3.0 mM. A hybrid layer of gold-zinc oxide (Au-ZnO) was used in this Kretschmann-based Surface Plasmon Resonance (K-SPR) technique with angular interrogation at 670 nm and 785 nm visible optical wavelengths. The purpose of this study is to observe the ability of the hybrid material as a sensing performance enhancer for differentiating between healthy and unhealthy uric acid levels based on the refractive index values from previous study. Upon exposure to 670 nm wavelength, the average sensitivity of this sensor was found to be 0.028°/mM with a linearity of 98.67 % and Q-factor value of 0.0053 mM-1. While at 785 nm, the average sensitivity is equal to 0.0193°/mM with slightly lower linearity at 94.46 % and Q-factor value of 0.0076 mM-1. The results have proven the ability of hybrid material Au-ZnO as a sensing performance enhancer for detecting uric acid when compared with bare Au and can be further explored in experimental work.