Improving inertial navigation systems with pedestrian locomotion classifiers
College of Computer Studies
PECCS 2015 - 5th International Conference on Pervasive and Embedded Computing and Communication Systems, Proceedings
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.
Digitial Object Identifier (DOI)
Ngo, C. M., See, S. L., & Legaspi, R. S. (2015). Improving inertial navigation systems with pedestrian locomotion classifiers. PECCS 2015 - 5th International Conference on Pervasive and Embedded Computing and Communication Systems, Proceedings, 202-208. https://doi.org/10.5220/0005242802020208
Computer Sciences | Software Engineering
Inertial navigation systems; Machine learning; Motion detectors