<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Friedrich, Björn</style></author><author><style face="normal" font="default" size="100%">Cauchi, Benjamin</style></author><author><style face="normal" font="default" size="100%">Hein, Andreas</style></author><author><style face="normal" font="default" size="100%">Fudickar, Sebastian</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Transportation mode classification from smartphone sensors via a long-short-term-memory network</style></title><secondary-title><style face="normal" font="default" size="100%">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</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">classification</style></keyword><keyword><style  face="normal" font="default" size="100%">imu</style></keyword><keyword><style  face="normal" font="default" size="100%">inertial</style></keyword><keyword><style  face="normal" font="default" size="100%">LSTM</style></keyword><keyword><style  face="normal" font="default" size="100%">Mode of Transportation</style></keyword><keyword><style  face="normal" font="default" size="100%">Phones</style></keyword><keyword><style  face="normal" font="default" size="100%">Supervised Machine Learning</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2019</style></date></pub-dates></dates><pages><style face="normal" font="default" size="100%">709 - 713</style></pages><isbn><style face="normal" font="default" size="100%">9781450368698</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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 &quot;GanbareAMT&quot; in the â€œSHL recognition challenge&quot;.</style></abstract></record></records></xml>