Sensor-Based Transportation Mode Recognition Using Variational Autoencoder

We present a data augmentation technique that can improve the classification of transportation modes when the training data is insufficient. The proposed method uses a variational autoencoder (VAE) based synthetic data generation algorithm for smartphone data. Often the data collected by individuals for research is limited due to practical constraints. The algorithm discussed would aid in generating similar data from a handful of collected data to give a substantial dataset for any machine learning models. We propose a VAE, the decoder of which can help generate this synthetic data. We show that the synthetic data closely follows the pattern of the real data. We also show that classification accuracy is improved with the use of this type of data. Our method would also be a useful tool to boost the samples of an underrepresented class in a dataset. The detection of activity recognition using smartphone sensors could be applied to multiple aspects of vehicle to pedestrian P2V systems and smart mobility.