Step Characterization using Sensor Information Fusion and Machine Learning

Ricardo Anacleto; Lino Figueiredo; Ana Almeida; Paulo Novais; António Meireles

doi:10.9781/ijimai.2015.357

Authors

Ricardo Anacleto University of Minho
Lino Figueiredo University of Minho
Ana Almeida University of Minho
Paulo Novais University of Minho
António Meireles University of Minho

DOI:

https://doi.org/10.9781/ijimai.2015.357

Keywords:

Localization, Learning, Machine Learning, Algorithms, Information Fusion

Abstract

A pedestrian inertial navigation system is typically used to suppress the Global Navigation Satellite System limitation to track persons in indoor or in dense environments. However, low- cost inertial systems provide huge location estimation errors due to sensors and pedestrian dead reckoning inherent characteristics. To suppress some of these errors we propose a system that uses two inertial measurement units spread in person’s body, which measurements are aggregated using learning algorithms that learn the gait behaviors. In this work we present our results on using different machine learning algorithms which are used to characterize the step according to its direction and length. This characterization is then used to adapt the navigation algorithm according to the performed classifications.

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