  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'.
