Transportation mode classification from smartphone sensors via a long-short-term-memory network
Title | Transportation mode classification from smartphone sensors via a long-short-term-memory network |
Publication Type | Journal Article |
Year of Publication | 2019 |
Authors | Friedrich B., Cauchi B., Hein A., Fudickar S. |
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 |
Pages | 709 - 713 |
Date Published | 2019 |
Publication Language | eng |
Keywords | classification, 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". |
DOI | 10.1145/3341162.3344855 |
Created at November 22, 2021 - 2:56pm by Fudickar.