Transportation Mode Classification from Smartphone Sensors via a Long-Short-Term-Memory Network

TL;DR

This paper presents a Long-Short-Term Memory network architecture for classifying transportation modes using smartphone sensor data, achieving a 63.68% F1-Score on internal testing, and participating in a recognition challenge.

Contribution

The paper introduces an LSTM-based model tailored for transportation mode classification from smartphone sensors, demonstrating its effectiveness in a competitive challenge.

Findings

Achieved 63.68% F1-Score on internal test data.

Successfully participated in the SHL recognition challenge.

Validated the effectiveness of LSTM for transportation mode classification.

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'.