Deep Convolutional Bidirectional LSTM based Transportation Mode Recognition [Conference Paper]

NESL Technical Report #: 2018-10-6


Abstract: Traditional machine learning approaches for recognizing modes of transportation rely heavily on hand-crafted feature extraction methods which require domain knowledge. In this paper, we propose a hybrid deep learning model: Deep Convolutional and Bidirectional-LSTM (DCBL) which combines convolutional layers and bidirectional LSTM layers. DCBL is trained directly on raw sensor data to predict the transportation modes. We compare our model to the traditional machine learning approaches of training Support Vector Machines and Multilayer Perceptron models on extracted features. In our experiments, DCBL performs better than the feature selection methods in terms of accuracy and simplifies the data processing pipeline. The models are trained on the Sussex-Huawei Locomotion-Transportation (SHL) dataset. The submission of our team, Vahan, to SHL recognition challenge uses an ensemble of DCBL models trained on raw data using the different combination of sensors and window

External paper URL

Publication Forum: Proceedings of the 6th International Workshop on Human Activity Sensing Corpus and Applications (HASCA2018)

Date: 2018-10-02

Public Document?: Yes

NESL Document?: Yes

Document category: Conference Paper

Primary Research Area: Mobile Phone based Sensing