Transportation mode classification from smartphone sensors via a long-short-term-memory network
November 22, 2021 - 2:56pm | 
| 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.
English
DeutschStudy Medical Informatics
at the University of Lübeck
Office
Susanne Petersen
Tel+49 451 3101 5601
Fax+49 451 3101 5604
Ratzeburger Allee 160
23538 Lübeck
Germany
Susanne Petersen
Tel+49 451 3101 5601
Fax+49 451 3101 5604
Ratzeburger Allee 160
23538 Lübeck
Germany