Combining LSTM and CNN for mode of transportation classification from smartphone sensors

Abstract

The broad availability of smartphones and Inertial Measurement Units in particular brings them into focus of recent research. Inertial Measurement Unit data is used for a variety of tasks. One important task is the classification of the mode of transportation. In this paper, we present a deep-learning-based algorithm, that combines long-short-term-memory (LSTM) layer and convolutional layer to classify eight different modes of transportation on the Sussex-Huawei Locomotion-Transportation (SHL) dataset. The inputs of our model are the accelerometer, gyroscope, linear acceleration, magnetometer, gravity and pressure values as well as the orientation information. We achieve a F1 score of 98.96 % on our private test set. We participated as team 103114102106|8 in the Sussex-Huawei Locomotion-Transportation (SHL) recognition challenge.