@article {1391,
	title = {Transportation mode classification from smartphone sensors via a long-short-term-memory network},
	journal = {UbiComp/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},
	year = {2019},
	month = {2019},
	pages = {709 - 713},
	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 {\^a}{\texteuro}{\oe}SHL recognition challenge".},
	keywords = {classification, imu, inertial, LSTM, Mode of Transportation, Phones, Supervised Machine Learning},
	isbn = {9781450368698},
	doi = {10.1145/3341162.3344855},
	author = {Friedrich, Bj{\"o}rn and Cauchi, Benjamin and Hein, Andreas and Fudickar, Sebastian}
}
