Learning Transportation Modes From Smartphone Sensors Based on Deep Neural Network

In recent years, the importance of user information has increased rapidly for context-aware applications. This paper proposes a deep learning mechanism to identify the transportation modes of smartphone users. The proposed mechanism is evaluated on a database that contains more than 1000 h of accelerometer, magnetometer, and gyroscope measurements from five transportation modes, including still, walk, run, bike, and vehicle. Experimental results confirm the effectiveness of the proposed mechanism, which achieves approximately 95% classification accuracy and outperforms four well-known machine learning methods. Meanwhile, we investigated the model size and execution time of different algorithms to address practical issues.