Transportation mode detection on mobile devices using recurrent nets

Toan H. Vu, Le Dung, Jia Ching Wang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

26 Scopus citations

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.

Original languageEnglish
Title of host publicationMM 2016 - Proceedings of the 2016 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Pages392-396
Number of pages5
ISBN (Electronic)9781450336031
DOIs
StatePublished - 1 Oct 2016
Event24th ACM Multimedia Conference, MM 2016 - Amsterdam, United Kingdom
Duration: 15 Oct 201619 Oct 2016

Publication series

NameMM 2016 - Proceedings of the 2016 ACM Multimedia Conference

Conference

Conference24th ACM Multimedia Conference, MM 2016
Country/TerritoryUnited Kingdom
CityAmsterdam
Period15/10/1619/10/16

Keywords

  • CGRNN
  • Control gate-based recurrent neural network

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