The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지

Gait State Classification by HMMS for Pedestrian Inertial Navigation System

보행용 관성 항법 시스템을 위한 HMMS를 통한 걸음 단계 구분

  • 박상경 (울산대학 전기전자정보시스템공학부) ;
  • 서영수 (울산대학 전기전자정보시스템공학부)
  • Published : 2009.05.01
Download PDF
⟨ Previous Next ⟩

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.

Keywords

References

  1. L. Ojeda, and J. Borenstein, 'Non-GPS Navigation for Security Personnel and First Responders,' The Journal of Navigation, Vol. 60, no. 3, pp. 391-407, 2007 https://doi.org/10.1017/S0373463307004286
  2. E. Foxlin, 'Pedestrian Tracking with Shoe-mounted Inertial Sensors,' IEEE Computer Graphics and Applications, Vol. 25, no. 6, pp. 38-46, 2005 https://doi.org/10.1109/MCG.2005.140
  3. X. Yun, E. R. Bachmann, H. Moore IV, and J. Calusdian, 'Self-contained Position Tracking of Human Movement Using Small Inertial/magnetic Sensor Modules,' in Proc. IEEE Int. Conf. Robotics and Automation, pp. 2526-2533, 2007 https://doi.org/10.1109/ROBOT.2007.363845
  4. K. Kong and M. Tomizuka, 'Smooth and Continuous Human Gait Phase Detection Based on Foot Pressure Patterns,' in Proc. IEEE Int. Conf. Robotics and Automation, pp. 3678-3683, 2008 https://doi.org/10.1109/ROBOT.2008.4543775
  5. B. D. O. Anderson, 'From Wiener to Hidden Markov Models,' IEEE Control Systems Magazine, Vol. 19, no. 3, pp. 41-51, 1999 https://doi.org/10.1109/37.768539
  6. L. Shue, B. D. O. Anderson, and S. Dey, 'Exponential Stability of Filters and Smoothers for Hidden Markov Models,' IEEE Trans. Acoustics, Speech, and Signal Processing, Vol. 46, no. 8, pp. 2180-2194, 1988 https://doi.org/10.1109/78.705429
  7. Perry, Gait Analysis: Normal and Pathological Function. SLACK Incorporated, 1992
  8. J. B. Kuipers, Quaternions and rotation sequences: a primer with applications to orbits, aerospace, and virtual reality. New Jersey: Princeton University Press, 1999
  9. R. G. Brown and P. Y. C. Hwang, Introduction to Random Signals and Applied Kalman Filtering, 3rd ed. New York: John Wiley & Sons, 1997