| Title |
Gait State Classification by HMMS for Pedestrian Inertial Navigation System |
| Authors |
박상경(Park, Sang-Kyeong) ; 서영수(Suh, Young-Soo) |
| Keywords |
Pedestrial Navigation System ; Zero Velocity Updating ; Hidden Markov Mode |
| Abstract |
An inertial navigation system for pedestrian position tracking is proposed, where the position is computed using inertial sensors mounted on shoes. Inertial navigation system(INS) errors increase with time due to inertial sensor errors, and therefore it needs to reset errors frequently. During normal walking, there is an almost periodic zero velocity instance when a foot touches the floor. Using this fact, estimation errors are reduced and this method is called the zero velocity updating algorithm. 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 each state is estimated using the hidden Markov model smoother. With this gait estimation, the zero or nearly zero velocity interval is more accurately estimated, which helps to reduce the position estimation error. |