This article introduces 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 Network with common preprocessing steps such as normalisation for classification tasks a F1-Score accuracy of 63.68\% was achieved with an internal test dataset. We participated as Team 'GanbareAM' in the 'SHL recognition challenge'.
@article{arxiv.1910.04739,
title = {Transportation Mode Classification from Smartphone Sensors via a Long-Short-Term-Memory Network},
author = {Björn Friedrich and Benjamin Cauchy and Andreas Hein and Sebastian Fudickar},
journal= {arXiv preprint arXiv:1910.04739},
year = {2019}
}