Transportation mode detection on mobile devices using recurrent nets

Toan H. Vu, Le Dung, Jia Ching Wang

研究成果: 書貢獻/報告類型會議論文篇章同行評審

31 引文 斯高帕斯(Scopus)

摘要

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.

原文???core.languages.en_GB???
主出版物標題MM 2016 - Proceedings of the 2016 ACM Multimedia Conference
發行者Association for Computing Machinery, Inc
頁面392-396
頁數5
ISBN(電子)9781450336031
DOIs
出版狀態已出版 - 1 10月 2016
事件24th ACM Multimedia Conference, MM 2016 - Amsterdam, United Kingdom
持續時間: 15 10月 201619 10月 2016

出版系列

名字MM 2016 - Proceedings of the 2016 ACM Multimedia Conference

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???event.eventtypes.event.conference???24th ACM Multimedia Conference, MM 2016
國家/地區United Kingdom
城市Amsterdam
期間15/10/1619/10/16

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