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Young and Elderly, Normal and Pathological Gait Analysis Using Frontal View Gait Video Data Based on the Statistical Registration of Spatiotemporal Relationship

  • Kosuke OkusaEmail author
  • Toshinari Kamakura
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9741)

Abstract

We study the problem of analyzing and classifying frontal view gait video data. In this study, we focus on the shape scale changing in the frontal view human gait, we estimate scale parameters using the statistical registration and modeling on a video data. To demonstrate the effectiveness of our method, we apply our model to young and elderly, normal and pathological gait analysis. As a result, our model shows good performance for the scale estimation and gait analysis.

Keywords

Shape analysis Gait analysis Scale estimation 

Notes

Acknowledgement

This work was supported by JSPS KAKENHI Grant Number 30636907, 40150031.

References

  1. 1.
    Barnich, O., Droogenbroeck, M.V.: Frontal-view gait recognition by intra-and inter-frame rectangle size distribution. Pattern Recogn. Lett. 30, 893–901 (2009)CrossRefGoogle Scholar
  2. 2.
    Borel, S., Schneider, P., Newman, C.J.: Video analysis software increases the interrater reliability of video gait assessments in children with cerebral palsy. Gait Posture 33(4), 727–729 (2011)CrossRefGoogle Scholar
  3. 3.
    Gage, J.R.: Gait analysis for decision-making in cerebral palsy. Bull. Hosp. Jt. Dis. Orthop. Inst. 43(2), 147–163 (1982)Google Scholar
  4. 4.
    Grunt, S., van Kampen, P.J., Krogt, M.M., Brehm, M.A., Doorenbosch, C.A.M., Becher, J.G.: Reproducibility and validity of video screen measurements of gait in children with spastic cerebral palsy. Gait Posture 31(4), 489–494 (2010)CrossRefGoogle Scholar
  5. 5.
    Kadaba, M.P., Ramakrishnan, H.K., Wootten, M.E.: Measurement of lower extremity kinematics during level walking. J. Orthop. Res. 8(3), 383–392 (1990)CrossRefGoogle Scholar
  6. 6.
    Lee, T.K.M., Belkhatir, M., Lee, P.A.: Fronto-normal gait incorporating accurate practical looming compensation. In: Pattern Recognition (2008)Google Scholar
  7. 7.
    Okusa, K., Kamakura, T.: A statistical registration of scale changing and moving objects with application to the human gait analysis. Bull. Jpn. Soc. Comput. Statist. 24(2) (2012) (in Japanese)Google Scholar
  8. 8.
    Okusa, K., Kamakura, T.: Fast gait parameter estimation for frontal view gait video data based on the model selection and parameter optimization approach. IAENG Int. J. Appl. Math. 43(4), 220–225 (2013)MathSciNetGoogle Scholar
  9. 9.
    Okusa, K., Kamakura, T.: Gait parameter and speed estimation from the frontal view gait video data based on the gait motion and spatial modeling. Int. J. Appl. Math. 43(1), 37–44 (2013)MathSciNetGoogle Scholar
  10. 10.
    Okusa, K., Kamakura, T., Murakami, H.: A statistical registration of scales of moving objects with application to walking data. Bull. Jpn. Soc. Comput. Stat. 23(2), 94–111 (2011) (in Japanese)Google Scholar
  11. 11.
    Olshen, R.A., Biden, E.N., Wyatt, M.P., Sutherland, D.H.: Gait analysis and the bootstrap. Ann. Stat. 17(4), 1419–1440 (1989)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Tamersoy, B.: Background subtraction - lecture notes (2009)Google Scholar
  13. 13.
    Zheng, S., Zhang, J., Huang, K., He, R., Tan, T.: Robust view transformation model for gait recognition. In: International Conference on Image Processing (ICIP), pp. 2073–2076 (2011)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Kyushu UniversityFukuokaJapan
  2. 2.Chuo UniversityTokyoJapan

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