TY - JOUR
KW - Localization
KW - Learning
KW - Machine Learning
KW - Algorithms
KW - Information Fusion
AU - Paulo Novais
AU - Ricardo Anacleto
AU - Lino Figueiredo
AU - Ana Almeida
AU - António Meireles
AB - 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.
IS - Regular Issue
M1 - 5
N2 - 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.
PY - 2015
SP - 53
EP - 60
T2 - International Journal of Interactive Multimedia and Artificial Intelligence
TI - Step Characterization using Sensor Information Fusion and Machine Learning
UR - http://www.ijimai.org/journal/sites/default/files/files/2015/11/ijimai20153_5_7_pdf_23101.pdf
VL - 3
SN - 1989-1660
ER -