Step Characterization using Sensor Information Fusion and Machine Learning

Author
Keywords
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.
Year of Publication
2015
Journal
International Journal of Interactive Multimedia and Artificial Intelligence
Volume
3
Issue
Regular Issue
Number
5
Number of Pages
53-60
Date Published
12/2015
ISSN Number
1989-1660
Citation Key
URL
http://www.ijimai.org/journal/sites/default/files/files/2015/11/ijimai20153_5_7_pdf_23101.pdf
DOI
10.9781/ijimai.2015.357
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