Transportation Mode Classification from Smartphone Sensors via a Long-Short-Term-Memory Network
This work addresses the problem of accurately identifying transportation modes for applications like activity tracking or urban planning, but it is incremental as it applies an existing method to a specific dataset.
The paper tackled transportation mode classification from smartphone sensor data using a Long-Short-Term-Memory network, achieving an F1-Score of 63.68% on an internal test dataset as part of the SHL recognition challenge.
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'.