Orange labs contribution to the Sussex-Huawei locomotion-transportation recognition challenge

In this paper, our team (Orange Labs) propose to address the task of the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge (2019), which consists in recognizing the user transportation mode based on the smartphone inertial sensors data, by using method of recurrent neural networks. The bidirectional LSTM architecture has been proposed to solve this challenge. The model was trained on rotation and translation invariant features in order to ignore the information of smartphone orientation and location. Our preliminary results show that the proposed method reaches the accuracy of 60.4% for the user transportation mode recognition by using hand validation data as testing data. Based on this SHL recognition challenge, it has been proposed to take advantage of the obtained model and try to integrate it into our continuous identity authentication project.