Wavelet denoising and fractal feature selection for classifying simulated earthquake signal from mobile phone accelerometer

This work is an initial study of the research that aims to help people by giving an information about the earthquake while it happens eventhough the phone is not connected to the internet. In this research, we identify the pattern of the simulated earthquake signal from the mobile phone accelerometer via machine learning. Before the data is processed into the classifier, static windowing and denoising was done to boost up the accuracy. Another fractal features are extracted from the pre-denoised data, which are the box counting dimension feature and the Hurst coefficient. The purpose of doing static windowing is to obtain more features so that we can have many potential useful attribute candidates as possible. Denoising with symlet wavelet is done to remove the noises which can worsen the classification accuracy. The classification is done using support vector machine and multilayer perceptron classifier with the accuracy of 81% and 82.15%, respectively.