PROJECT TITLE :

Stance-Phase Detection for ZUPT-Aided Foot-Mounted Pedestrian Navigation System

ABSTRACT:

Zero velocity updates (ZUPT) is a good method for the foot-mounted inertial pedestrian navigation systems. For the ZUPT technique to work properly, it's necessary to properly detect the stance phase of every gait cycle. An adaptive stance-section detection technique is proposed primarily based solely on an inertial sensor, that deals with the measurement fluctuations in swing and stance phases differently, and applies a Clustering algorithm to partition the potential gait phases into true and false clusters, thereby yielding a time threshold to eliminate the false gait phases. The roles of the detection parameters and the link between them are analyzed to supply some suggestions for parameter tuning. Detection performance is evaluated with multisubject experimental information collected at varying walking speeds. The analysis results show that the proposed detection method performs well within the presence of measurement fluctuations, which will build the detection of stance phases a lot of robust and the choice of detection parameters a lot of flexible.