An efficient CNN model for transportation mode sensing

Abstract

Artificial intelligence gradually finds its wider applications in mobile phones. For a better user experience, sensing users' activity or context accurately is important to enable intelligent mobile services. In this poster, we present a Convolutional Neural Network (CNN) model to detect a user's current mode of transport. Our model utilizes mobile sensor data such as accelerometer and gyroscope in the spectral domain as inputs in order to mitigate mobile phone placement and orientation factors. Encouraging experimental results show that the proposed scheme solves efficiently the problem of pose and orientation change in the transportation mode detection. In addition, our CNN model has a simplified structure, suitable for running on a mobile device with existing neural processing units (NPU) hardware capability.