Pedestrian inertial navigation with gait phase detection assisted zero velocity updating

Abstract

An inertial navigation system for pedestrian position tracking is proposed, where the position is computed using inertial and magnetic sensors on shoes. Using the fact that there is a zero velocity interval in each stride, estimation errors are reduced. When implementing this zero velocity updating algorithm, it is important to know when is the zero velocity interval. The gait states are modeled as a Markov process and gait state is estimated using the hidden Markov model filter. With this gait estimation, the zero velocity interval is more accurately estimated, which helps to reduce the position estimation error.