Automatic classification of pathological gait patterns using ground reaction forces and machine learning algorithms

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

Volumn , Issue , 2011, Pages 453-457

  Alaqtash, Murad ^a   Sarkodie Gyan, Thompson ^a   Yu, Huiying ^a   Fuentes, Olac ^a   Brower, Richard ^b   Abdelgawad, Amr ^b  

^a 500 W University Avenue   (United States)

^b TEXAS TECH UNIVERSITY HEALTH SCIENCES CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL NEURAL NETWORK; AUTOMATIC CLASSIFICATION; CEREBRAL PALSY; CLASSIFICATION ACCURACY; FEATURE SETS; FEATURES SELECTION; GAIT CLASSIFICATION; GAIT FEATURES; GAIT PATTERN; GROUND REACTION FORCES; LEAVE-ONE-OUT; MEDIOLATERAL; MULTIPLE SCLEROSIS; NEAREST NEIGHBOR CLASSIFIERS; VERTICAL FORCE;

DISCRETE WAVELET TRANSFORMS; GAIT ANALYSIS; LEARNING ALGORITHMS; NEURAL NETWORKS; STATISTICAL TESTS; THREE DIMENSIONAL;

FEATURE EXTRACTION;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CEREBRAL PALSY; COMPUTER ASSISTED DIAGNOSIS; FOOT; HUMAN; MECHANICAL STRESS; METHODOLOGY; MULTIPLE SCLEROSIS; NEUROLOGIC GAIT DISORDER; PATHOPHYSIOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; NEUROLOGIC DISEASE;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CEREBRAL PALSY; DIAGNOSIS, COMPUTER-ASSISTED; FOOT; GAIT DISORDERS, NEUROLOGIC; HUMANS; MULTIPLE SCLEROSIS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; STRESS, MECHANICAL;

EID: 84861931793     PISSN: 1557170X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2011.6090063     Document Type: Conference Paper

References (27)

1. D. Lai, P. Levinger, R. Begg, W. Gilleard, and M. Palaniswami, "Automatic recognition of gait patterns exhibiting patellofemoral pain syndrome using a support vector machine approach", IEEE Trans. Information Technology in biomedicine, vol. 13, issue 5, pp. 810-817, 2009.
2. R. Begg, and J. Kamruzzaman, "A machine learning approach for automated recognition of movement patterns using basic, kinetic and kinematic gait data", Journal of Biomechanics, vol. 38, pp. 401-408, 2005.
3. T. Takahashi, K. Ishida, D. Hirose, Y. Nagano, K. Okumiya, M. Nishinaga, Y. Doi, and H. Yamamoto, "Vertical ground reaction force shape is associated with gait parameters, timed up and go, and functional reach in elderly females", J. Rehabil. Med., vol. 36, pp. 42-45, 2004.
4. H. Gok, S. Ergin, and G. Yavuzer, "Kinetic and kinematic characteristics of gait in patients with medial knee arthrosis, " Acta Orthopaedica, vol. 73, issue 6, pp. 647-652, 2002.
5. R. Lafuente, J.M. Belda, J. Sanches-Lacuesta, C. Soler, and J. Prat, "Design and test of neural networks and statistical classifiers in computer aided movement analysis: a case study on gait analysis", Clinical Biomechincs, vol. 13, issue 3, pp. 216-229, Nov. 1997.
6. N. Mezghani, S. Husse, K. Boivin, K. Turcot, R. Aissaoui, N. Hagemeister, and J. de Guise, "Automatic classification of asymptomatic and osteoarthritis knee gait patterns using kinematic data features and the nearest neighbor classifier", IEEE Trans. Biomedical Engineering, vol. 55, issue 3, pp.1230-1232, March 2008.
7. M. Kohle, and D. Merkl, "Analyzing Human Gait Patterns for Malfunction Detection". In Proc. 2000 ACM symposium on Applied computing 1, ACM, New York, NY, 2000, pp. 41-45.
8. A. R. Ismail, and S. S. Asfour, "Discrete wavelet transform: a tool in smoothing kinematic data", Journal of Biomechanics, vol. 32, pp. 317-321, 1999.
9. R. K. Begg, M. Palaniswami, and B. Owen, "Support vector machines for automated gait classification", IEEE Trans. Biomedical Engineering, vol. 52, issue 5, pp. 828-838, May 2005.
10. L. Wang, T. Tan, W. Hu, and H. Ning, "Automatic gait recognition based on statistical shape analysis", IEEE Trans. Image Processing, vol. 12, issue 9, pp. 1120-1131, Sept. 2003.
11. M. A. Hanson, H. C. Powell, A. T. Barth, J. Lach, and M. Brandt-Pearce, "Neural network gait classification for on-body inertial sensors". Sixth International Workshop on Wearable and Implantable Body Sensor Networks, Berkeley, CA, June 2009, pp. 181-186.
12. J. Parkka, M. Ermes, P. Korpipaa, J. Mantyjarvi, J. Peltola, and I. Korhonen, "Activity classification using realistic data from wearable sensors", IEEE Trans. Information Technology in Biomedicine, vol. 10, issue 1, pp. 119-128, 2006.
13. W. L. Wu, F. C. Su, Y. M. Cheng, and Y. L. Chou, "Potential of the genetic algorithm neural network in the assessment of gait patterns in ankle arthrodesis", Ann. Biomed. Eng., vol. 29, issue 1, pp. 83-91, Jan. 2001.
14. S. R. Simon, "Quantification of human motion: gait analysis-benefits and limitations to its application to clinical problems", Journal of Biomechanics, vol. 37, issue 12, pp. 1869-1880, Dec. 2004.
15. T. Chau, "A review of analytical techniques for gait data. Part 1: fuzzy, statistical and fractal methods", Gait & Posture, vol. 13, pp. 49-66, 2001.
16. T. Chau, "A review of analytical techniques for gait data. Part 2: neural network and wavelet methods", Gait & Posture, vol. 13, pp. 102-120, 2001.
17. F. Dobson, M. Morris, R. Baker, and H. Graham, "Gait classification in children with cerebral palsy: A systematic review", Gait & Posture, vol. 25, pp. 140-152, 2007.
18. S. J. Preece, J. Y. Goulermas, L. Kenney, D. Howard, K. Meijer, and R. Crompton, "Activity identification using body-mounted sensors-a review of classification techniques", Physiol. Meas., vol. 30, pp. 1-33, 2009.
19. L. Jonesa, M. Beynonb, C. Holta, and S. Royc, "An application of the Dempster-Shafer theory of evidence to the classification of knee function and detection of improvement due to total knee replacement surgery", Journal of Biomechanics, vol. 39, pp. 2512-2520, 2006.
20. D. A. Winter, Biomechanics and Motor control of Human Movement. 4th ed., Hoboken, New Jersey: John Wiley & sons, 2009.
21. S. Russell, and P. Norvig, Artificial Intelligence a modern approach. 2nd ed., New Jersey: Prentice-Hall, 2003.
22. T. M. Mitchell, Machine Learning. 1st ed., McGraw-Hill, 1997.
23. S. Haykin, Neural Networks; a Comprehensive Foundation. Englewood Cliffs, NJ: Prentice-Hall, 1999.
24. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification. 2nd ed., New York: Wiley, 2001.
25. N. V. Chawla, K. W. Bowyer, L. O. Hall, and W. P. Kegelmeyer, "SMOTE: Synthetic Minority Over-sampling Technique", Journal of Artificial Intelligence Research, vol. 16, pp. 321-357, 2002.
26. D. L. Olson, and D. Delen, Advanced Data Mining Techniques. Springer, 2008.
27. M. Alaqtash, H. Yu, R. Brower, A. Abdelgawad, and T. Sarkodie-Gyan, "Application of wearable sensors for human gait analysis using fuzzy computational algorithm, " Engineering Applications of Artificial Intelligence, In Press, Available online May 2011.