Top > JRM > Gait Phase Detection Using Foot Acceleration for Estimating Gr ...

single-rb.php

JRM Vol.24 No.5 pp. 828-837
doi: 10.20965/jrm.2012.p0828
(2012)

Paper:

Views over last 60 days: 857

Gait Phase Detection Using Foot Acceleration for Estimating Ground Reaction Force in Long Distance Gait Rehabilitation

Kazuya Kawamura*1, Yuya Morita*2, Jun Okamoto*3,
Kohei Saito*2, Salvatore Sessa*2, Massimiliano Zecca*2,
Atsuo Takanishi*2, Shin-ichiro Takasugi*4, and Masakatsu G. Fujie*2

*1Chiba University, 1-33 Yayoi-cho, Inage-ku, Chiba-shi, Chiba 263-8522, Japan

*2Waseda University, 2-2 Wakamatsu-cho, Shinjuku-ku, Tokyo 162-8480, Japan

*3Tokyo Women’s Medical University, 8-1 Kawada-cho, Shinjuku-ku, Tokyo 162-8666, Japan

*4Kyushu University Hospital, 3-1-1 Maidashi, Higashi-ku, Fukuoka city, Fukuoka 812-8582, Japan

Received:
March 17, 2012
Accepted:
July 4, 2012
Published:
October 20, 2012
Creative Commons License
Keywords:
gait rehabilitation, gait phase detection, jerk, acceleration
Abstract
In gait rehabilitation, achieving a gait analysis method using a simple system during long-distance walking is important. This method is required to measure all gait parameters in a single measurement. In addition, it is required that the measurement system is not spatially constrained. Therefore, we have been developing a gait tracking system with acceleration sensors for long-distance gait rehabilitation. In this paper, we describe a gait phase detection method using foot acceleration data for estimating ground reaction force during long-distance gait rehabilitation. To develop this method, we focused on the jerk of each foot in vertical axis direction. Using two accelerometers mounted on the left and right feet, we carried out three experiments. First, we measured the jerk of each foot during a free gait to verify the relation with the walking speed. Second, we measured the jerk of each foot during walking faster than normal for each subject. We then compared these results with the results of first experiments. Finally, we measured the jerk of each foot during left-right asymmetrical walking. The results confirmed that gait phase could be detected using the jerk of each leg, calculated from acceleration data in vertical axis direction. In particular, the timing of Heel-contact / Toe-off could be obtained with an average error of 0.03 s. And as a preliminary study, we estimated the ground reaction force using the one of the results.
Cite this article as:
K. Kawamura, Y. Morita, J. Okamoto, K. Saito, S. Sessa, M. Zecca, A. Takanishi, S. Takasugi, and M. Fujie, “Gait Phase Detection Using Foot Acceleration for Estimating Ground Reaction Force in Long Distance Gait Rehabilitation,” J. Robot. Mechatron., Vol.24 No.5, pp. 828-837, 2012.
Data files:
References
  1. [1] K. Cerny, “A Clinical Method of Quantitative Gait Analysis: Suggestion from the Field,” Physical Therapy, Vol.63, No.7, pp. 1125-1126, 1983.
  2. [2] J. J. Brunnekreef, C. J. T. van Uden, S. van Moorsel, and J. G. M. Kooloos, “Reliability of videotaped observational gait analysis in patients with orthopedic impairments,” BMC Musculoskeletal Disorders, Vol.6, 2005.
  3. [3] M. Nomura, Y. Kumasaka, K. Kawamura, J. Okamoto, H. Kenmotsu, S. Takasugi, M. Hashizume, and M. G. Fujie, “Development of a gait analysis system – Measurement accuracy in indoor environment –,” Proc. of the 2009 JSME Conf. on Robotics and Mechatronics, 2A1-L13, 2009 (in Japanese).
  4. [4] J. Okamoto, M. Nomura, Y. Morita, K. Kawamura, H. Kenmotsu, S. Takasugi, M. Hashizume, and M. G. Fujie, “Development of a geostationary satellite type gait analysis system – Evaluation of 3D magnetic distortion distribution –,” Proc. of the 2010 JSME Conf. on Robotics and Mechatronics, 2A2-G29, 2010 (in Japanese).
  5. [5] Y. Morita, J. Okamoto, K. Kawamura, M. Seki, S. Sessa, M. Zecca, Z. Lin, H. Ishii, S. Takasugi, A. Takanishi, and M. G. Fujie, “Gait Analysis system of presumption of method to estimate right and left separated ground reaction force using acceleration sensor,” Proc. of 26th Annual Conf. of Life Support Technology, pp. 489-491, 2010 (in Japanese).
  6. [6] K. Takata and M. Abo, “Gait Evaluation with a small threedimensional accelerometer,” Jikei-idai, Vol.119, No.5, pp. 331-338, 2004 (in Japanese).
  7. [7] J. J. Kavanagh and H. B. Menz, “Accelerometry: A technique for quantifying movement patterns during walking,” Gait and Posture, Vol.28, pp. 1-15, 2008.
  8. [8] R. Moe-Nilssen, “Test-Retest Reliability of Trunk Accelerometry During Standing and Walking,” Archives of Physical Medicine and Rehabilitation, Vol.79, pp. 1377-1385, 1998.
  9. [9] M. Henriksen, H. Lund, R. Moe-Nilssen, H. Bliddal, and B. Danneskiod-Samsoe, “Test-retest reliability of trunk accelerometric gait analysis,” Gait and Posture, Vol.19, pp. 288-297, 2004.
  10. [10] R. Senden, B. Grimm, I. C. Heyligers, H. H. C. M. savelberg, and K. Meijer, “Acceleration-based gait test for healthy subject: Reliability and reference data”, Gait and Posture, 30, pp.192-196, 2009.
  11. [11] B. Auvinet, G. Berrut, C. Touzard, L.Moutel, N. Collet, D. Chaleil, and E. Barrey, “Reference data for normal subjects obtained with an accelerometric device,” Gait and Posture, Vol.16, pp. 124-134, 2002.
  12. [12] J. J. Kavanagh, R. S. Barrett, and S. Morrision, “Upper body accelerations during walking in healthy young and elderly men,” Gait and Posture, Vol.30, pp. 291-298, 2004.
  13. [13] M. D. L. Hylton, B. Menz, V. S. Fung, and S. R. Lord, “Walking speed, cadence and step length are selected to optimize the stability of head and pelvis accelerations,” Exerimental Brain Research, Vol.184, pp. 210-209, 2008.
  14. [14] H. B. Menz, S. R. Lord, and R. C. Fitzpatrick, “Acceleration patterns of the head and pelvis when walking on level and irregular surfaces,” Gait and Posture, Vol.18, pp. 35-46, 2003.
  15. [15] W. Zijlstra and A. L. Hof, “Assessment of spatio-temporal gait parameters from trunk accelerations during human walking,” Gait and Posture, Vol.18, pp. 1-10, 2003.
  16. [16] A. M. Sabatini, C. Martelloni, S. Scapellato, and F. Cavallo, “Assessment of Walking Features From Foot Inertial Sensing,” IEEE Trans. on Biomedical Engineering, Vol.52, No.3, pp. 486-494, 2005.
  17. [17] S. J. M. Bamberg, A. Y. Benbasat, D. M. Scarborough, D. E. Krebs, and J. A. Paradiso, “Gait Analysis Using a Shoe-Integrated Wireless Sensor System,” IEEE Trans. of Information Technology in Biomedicine, Vol.12, No.4, pp. 413-423, 2008.
  18. [18] C. M. Senanayake and S. M. N. Arosha Senanayake, “Computational Intelligent Gait-Phase Detection System to Identify Pathological Gait,” IEEE Trans. of Information Technology in Biomedicine, Vol.14, No.5, pp. 1173-1179, 2010.
  19. [19] J. Bae and M. Tomizuka, “Gait phase analysis based on a Hidden Markov Model,” Mechatronics, Vol.20, pp. 961-970, 2011.
  20. [20] M. Zecca, S. Sessa, Z. Lin, T. Sasaki, T. Suzuki, K. Itoh, H. Iseki, and A. Takanishi, “Development of an Ultra-Miniaturized Inertial Measurement Unit for Objective Skill Analysis and Assessment in Neurosurgery: preliminary results,” MICCAI 2009, Part I, Lecture Notes in Computer Science, Vol.5761, pp. 443-500, 2009.
  21. [21] Z. Lin, M. Zecca, S. Sessa, L. Bartolomeo, H. Ishii, K. Itoh, and A. Takanishi, “Development of the miniaturized wireless Inertial Measurement Unit WB-4: Pilot test for mastication analysis,” in 2010 IEEE/SICE Int. Symposium on System Integration (SII), pp. 420-425, 2010.
  22. [22] Y. Mukaeda, Y. Suzuki, Z. Lin, L. Bartolomeo, K. Itoh, H. Ishii, S. Sessa, M. Zecca, and A. Takanishi, “Performance evaluation of wireless ultra-miniaturized Inertial Measurement Unit,” in SICE System Integration 2010, Sendai, Japan, Vol.12, 2010 (in Japanese).
  23. [23] M. Hanlon and R. Anderson “Real-time gait event detection using wearable sensors,” Gait and Posture, Vol.30, Issue 4, pp. 523-527, 2009.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

Copyright© 2012 by Fuji Technology Press Ltd. and Japan Society of Mechanical Engineers. All right reserved.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Apr. 19, 2024