Transportation Mode Detection on Mobile Devices Using Recurrent Nets

Abstract

We present an approach to the use of Recurrent Neural Networks (RNN) for transportation mode detection (TMD) on mobile devices. The proposed model, called Control Gate-based Recurrent Neural Network (CGRNN), is an end-to-end model that works directly with raw signals from an embedded accelerometer. As mobile devices have limited computational resources, we evaluate the model in terms of accuracy, computational cost, and memory usage. Experiments on the HTC transportation mode dataset demonstrate that our proposed model not only exhibits remarkable accuracy, but also is efficient with low resource consumption.