Improving inertial navigation systems with pedestrian locomotion classifiers

• Main Authors: Ngo, Courtney Anne M., See, Solomon L., Legaspi, Roberto S.
• Format: text
• Published: Animo Repository 2015
• Subjects: Inertial navigation systems; Machine learning; Motion detectors; Computer Sciences; Software Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/3512; https://animorepository.dlsu.edu.ph/context/faculty_research/article/4514/type/native/viewcontent/0005242802020208.html
• Institution: De La Salle University

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.