@inproceedings{4b33cdaec3644eab853061573680cdcb,
title = "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 Gatebased Recurrent Neural Network (CGRNN), is an end-toend 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.",
keywords = "CGRNN, Control gate-based recurrent neural network",
author = "Vu, {Toan H.} and Le Dung and Wang, {Jia Ching}",
note = "Publisher Copyright: {\textcopyright} 2016 ACM.; 24th ACM Multimedia Conference, MM 2016 ; Conference date: 15-10-2016 Through 19-10-2016",
year = "2016",
month = oct,
day = "1",
doi = "10.1145/2964284.2967249",
language = "???core.languages.en_GB???",
series = "MM 2016 - Proceedings of the 2016 ACM Multimedia Conference",
publisher = "Association for Computing Machinery, Inc",
pages = "392--396",
booktitle = "MM 2016 - Proceedings of the 2016 ACM Multimedia Conference",
}