MIDL 2021

 
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5. bis 7. Juli 2021

Transportation mode classification from smartphone sensors via a long-short-term-memory network

TitelTransportation mode classification from smartphone sensors via a long-short-term-memory network
Publication TypeJournal Article
Year of Publication2019
AuthorsFriedrich B., Cauchi B., Hein A., Fudickar S.
JournalUbiComp/ISWC 2019- - Adjunct Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers
Pages709 - 713
Date Published2019
Publication Languageeng
Schlüsselwörterclassification, imu, inertial, LSTM, Mode of Transportation, Phones, Supervised Machine Learning
Abstract

This article introduce the architecture of a Long-Short-Term-Memory network for classifying transportation-modes via smartphone data and evaluates its accuracy. By using a Long-Short-Term-Memory with common preprocessing steps such as normalisation for classification tasks an F1-Score accuracy of 63.68 % was achieved with an internal test dataset. We participated as team "GanbareAMT" in the “SHL recognition challenge".

DOI10.1145/3341162.3344855
Erstellt am 22. November 2021 - 14:56 von Fudickar.

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