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The Scientific World Journal
Volume 2014 (2014), Article ID 861529, 10 pages
http://dx.doi.org/10.1155/2014/861529
Research Article

Identification of the Causative Disease of Intermittent Claudication through Walking Motion Analysis: Feature Analysis and Differentiation

1The School of Mechanical Engineering, Kanazawa University, Kakuma-machi, Kanazawa 920-1192, Japan
2Department of Orthopedic Surgery, Graduate School of Medical Science, Kanazawa University, Japan
3Department of Orthopedic Surgery, Koseiren Takaoka Hospital, Japan

Received 19 March 2014; Accepted 19 June 2014; Published 7 July 2014

Academic Editor: Nizar Bouguila

Copyright © 2014 Tetsuyou Watanabe et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. Y. Toribatake, “Classification and differential diagnosis of intermittent claudication,” Journal of Spine and Spinal Cord, vol. 21, no. 4, pp. 333–340, 2008 (Japanese). View at Google Scholar
  2. Y. Toribatake, E. Sawamura, N. Kano, K. Kitagawa, and Y. Saito, “The frequency and differential diagnosis of peripheral arterial occlusive disease in intermittent claudicants in orthopaedics,” Orthopaedic Surgery and Traumatology, vol. 45, no. 6, pp. 665–674, 2002 (Japanese). View at Google Scholar
  3. T. Watanabe, Y. Sanou, T. Yoneyama, Y. Toribatake, H. Hayashi, and N. Yokogawa, “Walking motion analysis of intermittent claudication and its application to medical diagnosis,” in Proceedings of the 3rd IEEE RAS and EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob '10), pp. 448–453, September 2010. View at Publisher · View at Google Scholar · View at Scopus
  4. Y. Toribatake and N. Komine, “Usefulness of stress-loading test for ankle brachial index using an originally developed exercise device to detect peripheral arterial disease,” International Angiology, vol. 28, no. 2, pp. 100–105, 2009. View at Google Scholar · View at Scopus
  5. B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A training algorithm for optimal margin classifiers,” in Proceedings of the 5th Annual Workshop on Computational Learning Theory, pp. 144–152, ACM, 1992.
  6. C. Cortes and V. Vapnik, “Support-vector networks,” Machine Learning, vol. 20, no. 3, pp. 273–297, 1995. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  7. T. Watanabe, T. Yoneyama, Y. Toribatake, H. Hayashi, and N. Yokogawa, “Study on differentiation factors for main disease identification of intermittent claudication,” in Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC '12), pp. 4696–4699, San Diego, Calif, USA, August-September 2012. View at Publisher · View at Google Scholar
  8. Y. Suda, M. Saitou, K. Shibasaki, N. Yamazaki, K. Chiba, and Y. Toyama, “Gait analysis of patients with neurogenic intermittent claudication,” Spine, vol. 27, no. 22, pp. 2509–2513, 2002. View at Publisher · View at Google Scholar · View at Scopus
  9. N. C. Papadakis, D. G. Christakis, G. N. Tzagarakis et al., “Gait variability measurements in lumbar spinal stenosis patients: part A. Comparison with healthy subjects,” Physiological Measurement, vol. 30, no. 11, pp. 1171–1186, 2009. View at Publisher · View at Google Scholar · View at Scopus
  10. N. C. Papadakis, D. G. Christakis, G. N. Tzagarakis et al., “Gait variability measurements in lumbar spinal stenosis patients: part B. Preoperative versus postoperative gait variability,” Physiological Measurement, vol. 30, no. 11, pp. 1187–1195, 2009. View at Publisher · View at Google Scholar · View at Scopus
  11. N. Yokogawa, Y. Toribatake, H. Murakami et al., “Evaluation of differences in the gait characteristics of patients with lumbar spinal canal stenosis (l4 radiculopathy) and osteoarthritis of the hip by using a new motion analysis method,” in Proceedings of EuroSpine, 2012.
  12. S. A. Scherer, J. S. Bainbridge, W. R. Hiatt, and J. G. Regensteiner, “Gait characteristics of patients with claudication,” Archives of Physical Medicine and Rehabilitation, vol. 79, no. 5, pp. 529–531, 1998. View at Publisher · View at Google Scholar · View at Scopus
  13. S. A. Myers, I. I. Pipinos, J. M. Johanning, and N. Stergiou, “Gait variability of patients with intermittent claudication is similar before and after the onset of claudication pain,” Clinical Biomechanics, vol. 26, no. 7, pp. 729–734, 2011. View at Publisher · View at Google Scholar · View at Scopus
  14. A. W. Gardner, L. Forrester, and G. V. Smith, “Altered gait profile in subjects with peripheral arterial disease,” Vascular Medicine, vol. 6, no. 1, pp. 31–34, 2001. View at Publisher · View at Google Scholar · View at Scopus
  15. T. Chau, “A review of analytical techniques for gait data. Part 1: fuzzy, statistical and fractal methods,” Gait & Posture, vol. 13, no. 1, pp. 49–66, 2001. View at Publisher · View at Google Scholar · View at Scopus
  16. T. Chau, “A review of analytical techniques for gait data. Part 2: neural network and wavelet methods,” Gait and Posture, vol. 13, no. 2, pp. 102–120, 2001. View at Publisher · View at Google Scholar · View at Scopus
  17. J.-S. Wang, C.-W. Lin, Y.-T. Yang, and Y.-J. Ho, “Walking pattern classification and walking distance estimation algorithms using gait phase information,” IEEE Transactions on Biomedical Engineering, vol. 59, no. 10, pp. 2884–2892, 2012. View at Google Scholar
  18. J. Kamruzzaman and R. K. Begg, “Support vector machines and other pattern recognition approaches to the diagnosis of cerebral palsy gait,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 12, pp. 2479–2490, 2006. View at Publisher · View at Google Scholar
  19. N. Mezghani, S. Husse, K. Boivin et al., “Automatic classification of asymptomatic and osteoarthritis knee gait patterns using kinematic data features and the nearest neighbor classifier,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 3, pp. 1230–1232, 2008. View at Publisher · View at Google Scholar · View at Scopus
  20. B. D. Lucas and T. Kanade, “An iterative image registration technique with an application to stereo vision,” in Proceedings of the Imaging Understanding Workshop, pp. 121–130, 1981.
  21. R. Putz, R. Pabst, A. H. Weiglein, and A. N. Taylor, Sobotta Atlas of Human Anatomy, Lippincott Williams and Wilkins, 2001.
  22. T. Sakai and N. Hashimoto, “Anatomical chart everyone can understand,” Narumido, 2010. View at Google Scholar
  23. M. A. Oskoei and H. Hu, “Support vector machine-based classification scheme for myoelectric control applied to upper limb,” IEEE Transactions on Biomedical Engineering, vol. 55, no. 8, pp. 1956–1965, 2008. View at Publisher · View at Google Scholar
  24. C.-C. Chang and C.-J. Lin, “LIBSVM: a library for support vector machines,” ACM Transactions on Intelligent Systems and Technology, vol. 2, pp. 1–27, 2011. View at Google Scholar
  25. C. M. Bishop, Pattern Recognition and Machine Learning, Information Science and Statistics, Springer, New York, NY, USA, 2006. View at Publisher · View at Google Scholar · View at MathSciNet
  26. C.-W. Hsu and C.-J. Lin, “A comparison of methods for multiclass support vector machines,” IEEE Transactions on Neural Networks, vol. 13, no. 2, pp. 415–425, 2002. View at Publisher · View at Google Scholar
  27. T. Yoshikawa, Foundations of Robotics, MIT Press, Cambridge, Mass, USA, 1990. View at MathSciNet