Document Type: Original Article


Department of Electrical Engg, MITS, Gwalior


The aim of this work is to use Self Organizing Map (SOM) for clustering of locomotion kinetic characteristics in normal and Parkinson’s disease. The classification and analysis of the kinematic characteristics of human locomotion has been greatly increased by the use of artificial neural networks in recent years. The proposed methodology aims at overcoming the constraints of traditional analysis methods and to find new clinical ways for observing the large amount of information obtained in a gait lab. Self organizing maps (SOM) also called Kohonen maps are a special kind of neural networks that can be used for clustering tasks. The results are shown in the terms of sensitivity, specificity, accuracy, error rate from the two groups of features which are the Mean Coefficient of Variation and Mean Sum of Variation and Mean Max and Mean Standard deviation of the Ground Reaction Force. Results showing the potential of this technique for distinguishing between population of individuals with normal gait and with gait disorders of different causes of disease


1. Physiobank. Physiological signal archives for biomedical research. [Internet]. PhysioNet: MIT; Cambridge; 2009. [Cited 2011 Mar 20]. Available from: http://www.physionet.Org/physiobank/ database /gaitdb/
2. National Parkinson Foundation http:// www. Parkinson. Org.
3. Simon SR. Quantification of human motion: gait analysis -benefits and limitations to its application to clinical problems. Journal of Biomechanics. 2004; 37(12):1869-80.Pmid: 15519595.http:// dx .doi .org /10 .1016/j. jbiomech. 2004.02.047
4. Oana Geman, Ioan Ungurean, Valentin Popa, Cornel Octavian Turcu, Nicoleta-Cristina Găitan. Gait in Parkinson's Disease - signal processing and modeling. 11th International Conference on development and application system, Suceava, Romania.
5. Silvia Elizabeth Rodrigo*, Claudia Noemí Lescano, Rodolfo Horacio Rodrigo. Application of Kohonen maps to kinetic analysis of human gait. Engenharia Biomedical Brazilian Journal of Biomedical Engineering. Volume 28, Número 3, p. 217-226, 2012
6. Hoehn MM, Yahr MD. Parkinsonism: onset, progression, and mortality. Neurology. 1967; 17(5):427- 42. PMid: 6067254.
7. Crowther RG, Spinks WL, Leicht AS, Quigley F, Golledge J. Intralimb coordination variability in peripheral arterial disease. Clinical Biomechanics. 2008; 23(3):357-64. PMid: 18061322.
8. Koozekanani SH, Balmaseda MT, Fatehi MT, Lowney ED. Ground reaction forces during ambulation in Parkinsonism: pilot study. Archives of Physical Medicine and Rehabilitation. 1987; 68(1): 28-30.
PMid: 3800620.
9. Sanchez Lacuesta JJ, Prat Pastor JM, Hoyos Fuentes JV, Viosca Herrero E, Soler Gracia C, Comín Clavijo M, Lafuente Jorge R, Fabregat A, Vera PP. Biomecánica de la marcha humana normal y patológica. Valencia: Instituto de Biomecánica de Valencia; 1999.
10. Chau T. A review of analytical techniques for gait data. Part 2: neural network and wavelet methods. Gait & Posture. 2001; 13(2):102-20. 6362(00)00095-3
11. Köhle M, Merkl D. Identification of gait patterns with self-organizing maps based on ground reaction force. In: ESANN’96: Proceedings of the European Symposium on Artificial Neural Networks; 1996 Apr 24-26; Bruges, Belgium. Bruges; 1996. p. 73-8.
12. Barton JG, Lees A. An application of neural networks for distinguishing gait patterns on the basis of hip-knee joint angle diagrams. Gait & Posture. 1997; 5(1):28-33. http://
13. Barton JG, Lees A, Lisboa G, Attfield S. Visualisation of gait data with Kohonen self-organising neural maps. Gait & Posture. 2006;24(1):46-53. gaitpost.2005.07.005
14. Battistella ME, Lescano CN, Rodrigo RH, Rodrigo SE. Registrador de presiones plantares en condiciones dinámicas. Memorias del XVII Congreso Argentino de Bioingeniería y las VI Jornadas de Ingeniería Clínica; 2009 Oct 14-16; Rosario, Argentina. 2009. p. 150-3.
15. Bates BT, James CR, Dufek JS. Single-Subject Analysis. In: Stergiou N, editor. Innovative tools for human movement research. Human Kinetics; 2004. p. 3-28.
16. Bernstein N. The coordination and regulation of movement. London: Pergamon Press; 1967.
17. Hausdorff JM. Gait variability: methods, modeling and meaning. Journal of Neuro Engineering and Rehabilitation. 2005; 2:1-9. PMid: 16033650 PMCid: 1185560. http://dx.doi. org/10.1186/1743-0003-2-19.
18. Hausdorff JM, Lertratanakul A, Cudkowicz ME, Peterson AM, Kaliton D, Goldberger AL. Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. Journal of Applied Physiology. 2000; 88:2045-2053. PMid: 10846017.
19. Hausdorff JM, Schaafsma JD, Balash Y, Bartels AL, Gurevich T, Giladi N. Impaired regulation of stride variability in Parkinson’s disease subjects with freezing of gait. Experimental Brain Research. 2003; 149(2): 187-94. PMid: 12610686.
20. Davie CA. A review of Parkinson’s disease. British Medical Bulletin. 2008; 86(1):109-127. PMid: 18398010. http://
21. James CR. Considerations of Movement Variability in Biomechanics Research. In: Stergiou N, editor. Innovative Tools for Human Movement Research. United States of America: Human Kinetics; 2004. p. 29-62.
22. Haykin S. Self-organizing Maps. In: Haykin S, editor. Neural Networks. A Comprehensive Foundation. Delhi: Pearson Education; 2005. p. 465-505.
23. Kohonen T. Self-organizing maps. Berlin: Springer; 2001. 56927-2
24. Hausdorff JM, Cudkowicz ME, Firtion R, Wei JY, Goldberger AL. Gait variability and basal ganglia disorders: stride-to-stride variations in gait cycle timing in Parkinson’s disease and Huntington’s disease. Movement Disorders. 1998; 13(3):428-37. PMid: 9613733.