Improving inertial navigation systems with pedestrian locomotion classifiers

College

College of Computer Studies

Department/Unit

Software Technology

Document Type

Conference Proceeding

Source Title

PECCS 2015 - 5th International Conference on Pervasive and Embedded Computing and Communication Systems, Proceedings

First Page

202

Last Page

208

Publication Date

1-1-2015

Abstract

Researches on inertial navigation systems (INS) have formulated complex step detection algorithms and stride length estimations. But for current systems to work, INSs have to correctly identify negative pedestrian locomotion. Negative pedestrian locomotion are movements that a user can naturally make without any real position displacement, but has sensor signals that might be misidentified as steps. As the INS's modules have a cascading nature, it is important that these false movements are identified beforehand. This research aims to provide a solution by studying patterns exhibited by positive and negative pedestrian locomotion when sensors are placed on a user's front pocket. A model was then built to classify negative from positive pedestrian locomotion, and to improve the INS's accuracy overall.

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Digitial Object Identifier (DOI)

10.5220/0005242802020208

Disciplines

Computer Sciences | Software Engineering

Keywords

Inertial navigation systems; Machine learning; Motion detectors

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