Locomotion Recognition Using XGBoost and Neural Network Ensemble

Mode of transport recognition is an important part of understanding the context of a person with a mobile phone being the best device on which to do this since a person typically carries it around for most of the day. In this paper, we present a method to understand the mode of transport (also called locomotion recognition) using data collected from Android mobile phones. The method is the submitted solution for "Team Jellyfish" for the Sus sex-Huawei Locomotion-Transportation recognition challenge. The goal is to develop a body-position independent classifier that uses data from a set of commonly available sensors on a phone - accelerometer, gyroscope, magnetometer and barometer. The solution is an ensemble of XGBoost and neural network classifiers. The precision and recall using 5-fold cross validation on the validation set is 0.946 and 0.945 respectively.