DOI
10.9781/ijimai.2015.357
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.
Source Publication
International Journal of Interactive Multimedia and Artificial Intelligence
Recommended Citation
Novais, Paulo; Anacleto, Ricardo; Figueiredo, Lino; Almeida, Ana; and Meireles, António
(2015)
"Step Characterization using Sensor Information Fusion and Machine Learning,"
International Journal of Interactive Multimedia and Artificial Intelligence: Vol. 3:
Iss.
5, Article 5.
DOI: 10.9781/ijimai.2015.357
Available at:
https://ijimai.researchcommons.org/ijimai/vol3/iss5/5
