Back to Search
Start Over
Transportation mode classification from smartphone sensors via a long-short-term-memory network
- Source :
- UbiComp/ISWC Adjunct
- Publication Year :
- 2019
- Publisher :
- ACM, 2019.
-
Abstract
- 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'.<br />5 pages, 6 figures, 2 tables, ubicomp19
- Subjects :
- Signal Processing (eess.SP)
FOS: Computer and information sciences
Computer Science - Machine Learning
Computer science
010401 analytical chemistry
Real-time computing
020207 software engineering
02 engineering and technology
01 natural sciences
Machine Learning (cs.LG)
0104 chemical sciences
Test (assessment)
Long short term memory
Mode (computer interface)
Inertial measurement unit
FOS: Electrical engineering, electronic engineering, information engineering
0202 electrical engineering, electronic engineering, information engineering
Preprocessor
Electrical Engineering and Systems Science - Signal Processing
Architecture
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 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
- Accession number :
- edsair.doi.dedup.....564659cb97572ac43617ec99b0519ec9