Foot Motion Estimation Smoother using Inertial Sensors

관성센서를 사용한 발의 움직임 추정용 평활기

  • Received : 2011.12.13
  • Accepted : 2012.04.03
  • Published : 2012.05.01


A foot motion is estimated using an inertial sensor unit, which is installed on a shoe. The inertial sensor unit consists of 3 axis accelerometer and 3 axis gyroscopes. Attitude and position of a foot are estimated using an inertial navigation algorithm. To increase estimation performance, a smoother is used, where the smoother employs a forward and backward filter structure. An indirect Kalman filter is used as a forward filter and backward filter. A new combining algorithm for the smoother is proposed to combine a forward indirect Kalman filter and a backward indirect Kalman filter. Through experiments, the estimation performance of the proposed smoother is verified.


Supported by : 울산대학교


  1. M. W. Whittle, Gait Analysis: An Introduction, Elsevier, New York, 2008.
  2. D. T. Nhut and Y. S. Suh, "Gait analysis system using infrared LED landmarks," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 17, no. 7, pp. 641-646, 2011.
  3. D. H. Titterton and J. Weston, Strapdown Inertial Navigation Technology, AIAA, 2005.
  4. A. M. Sabatini et al, "Assessment of walking features from foot inertial sensing," IEEE Tran. on Biomedical Engineering, vol. 52, no. 3, pp. 486-494, 2005.
  5. A. M. Sabatini, "Quaternion based attitude estimation algorithm applied to signals from body-mounted gyroscopes," Electronics Letters, vol. 40, no. 10, pp. 584-586, 2004.
  6. L. Ojeda and J. Borenstein, "Personal dead-reckoning system for GPS-denied environments," Proc. of IEEE International Workshop on Safety, Security and Rescue Robotics, pp. 1-6, 2007.
  7. E. Foxlin, "Pedestrian tracking with shoe-mounted inertial sensors," IEEE Computer Graphics and Applications, vol. 25, no. 6, pp. 38-46, 2005.
  8. R. G. Brown and P. Y. C. Hwang, Introduction to Random Signals and Applied Kalman Filtering, John Wiley & Sons, 1997.
  9. Y. S. Suh, T. N. Do, Y. S. Ro, and H. J. Kang, "A smoother for attitude estimation using inertial and magnetic sensors," in Proc. of 2010 IEEE Sensors, pp. 743-746, 2010.
  10. S. I. Roumeliotis, G. S. Sukhatme, and G. A. Bekey, "Smoother based 3D attitude estimation for mobile robot localization," in Proc. of the 1999 IEEE International Conference on Robotics & Automation, vol. 3, pp. 1979-1986, 1999.
  11. Y. S. Suh, "A smoother for attitude and position estimation using inertial sensors with zero velocity intervals," IEEE Sensors Journal, to appear.
  12. J. B. Kuipers, Quaternions and Rotation Sequences: A Primer With Applications to Orbits, Aerospace, and Virtual Reality, Princeton Univ. Press, 1999.
  13. S. P. Won and F. Golnaraghi, "A triaxial accelerometer calibration method using a mathematical model," IEEE Tr. on Instrumentation and Measurement, vol. 59, no. 8, pp. 2144-2153, 2010.
  14. O. Woodman, "Pedestrian localisation for indoor environments," Ph.D. thesis, University of Cambridge, Computer Laboratory, Sep. 2010.
  15. M. D. Shuster and S. D. Oh, "Three-axis attitude determination from vector observations," Journal of Guidance and Control, vol. 4, no. 1, pp. 70-77, 1981.
  16. F. L. Markley, "Multiplicative vs. additive filtering for spacecraft attitude determination," 6th Cranfield Conference on Dynamics and Control of Systems and Structures in Space, Cranfield, Bedford, UK, pp. 467-474, 2004.
  17. Y S. Suh, "Orientation estimation using a quaternionbased indirect Kalman filter with adaptive estimation of external acceleration," IEEE Tran. on Instrumentation and Measurement, vol. 59, no. 12, pp. 3296-3304, 2010.
  18. O. Shin and C. G. Park, "Pose estimation of ground test bed using ceiling landmark and optical flow based on single Camera/IMU Fusion," Journal of Institute of Control, Robotics and Systems (in Korean), vol. 18, no. 1, pp. 54-61, 2012.

Cited by

  1. Enhanced Attitude Determination with IMU using Estimation of Lever Arms vol.19, pp.10, 2013,