Björn Friedrich
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".
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Index Terms
- Transportation mode classification from smartphone sensors via a long-short-term-memory network
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