A novel approach for transportation mode detection: Combining t-SNE Manifold Learning and Support Vector Machines

Abstract

The aim of this study is to detect transportation modes by using smart phone sensor data. The data are obtained from the GPS, accelerometer and gyroscope sensors of the smartphone. The collected data is divided into 10 second windows and each pattern contains 200 patterns. After the attributes have been determined, the manifold learning algorithm is applied to data set. The obtain features are classified by the Support Vector Machine (SVM) method. In experimental study stage, the performances of three kernel functions of the SVM were compared.