Diagnosing Huntington’s Disease Through Gait Dynamics

  • Juliana Paula FelixEmail author
  • Flávio Henrique Teles Vieira
  • Ricardo Augusto Pereira Franco
  • Ronaldo Martins da Costa
  • Rogerio Lopes Salvini
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11845)


This study proposes an automatic method for identifying Huntington’s disease using features extracted from gait signals derived from force-sensitive resistors. Features were extracted using metrics of fluctuation magnitude and fluctuation dynamics, obtained from a detrended Fluctuation Analysis (DFA). In the classification, five machine learning algorithms (Support Vector Machines (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), Linear Discriminant Analysis (LDA) and Decision Tree (DT)) were compared by the leave-one-out cross-validation method. Our experiments showed that SVM and DT provided the best results, achieving an average accuracy of 100.0%, representing an improvement compared to other results in the literature, and proving the effectiveness of the proposed method.


Automatic diagnosis Huntington’s disease Machine learning Gait dynamics 


  1. 1.
    Alexander, N.B.: Gait disorders in older adults. J. Am. Geriatr. Soc. 44(4), 434–451 (1996). Scholar
  2. 2.
    Altman, D.G., Bland, J.M.: Diagnostic tests. 1: sensitivity and specificity. BMJ: Br. Med. J. 308(6943), 1552 (1994).
  3. 3.
    America, H.D.S.: Overview of Huntington’s disease.
  4. 4.
    Aziz, W., Arif, M.: Complexity analysis of stride interval time series by threshold dependent symbolic entropy. Eur. J. Appl. Physiol. 98(1), 30–40 (2006). Scholar
  5. 5.
    Baratin, E., Sugavaneswaran, L., Umapathy, K., Ioana, C., Krishnan, S.: Wavelet-based characterization of gait signal for neurological abnormalities. Gait Posture 41(2), 634–639 (2015). Scholar
  6. 6.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York (2006)zbMATHGoogle Scholar
  7. 7.
    Daliri, M.R.: Automated diagnosis of Alzheimer disease using the scale-invariant feature transforms in magnetic resonance images. J. Med. Syst. 36(2), 995–1000 (2012). Scholar
  8. 8.
    Daliri, M.R.: Automatic diagnosis of neuro-degenerative diseases using gait dynamics. Measurement 45(7), 1729–1734 (2012). Scholar
  9. 9.
    Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification, 2nd edn. Wiley-Interscience, Hoboken (2000)zbMATHGoogle Scholar
  10. 10.
    Foody, G.M., Mathur, A.: A relative evaluation of multiclass image classification by support vector machines. IEEE Trans. Geosci. Remote Sens. 42(6), 1335–1343 (2004). Scholar
  11. 11.
    Goldfarb, B., Simon, S.: Gait patterns in patients with amyotrophic lateral sclerosis. Arch. Phys. Med. Rehabil. 65(2), 61–65 (1984)Google Scholar
  12. 12.
    Gupta, K., Khajuria, A., Chatterjee, N., Joshi, P., Joshi, D.: Rule based classification of neurodegenerative diseases using data driven gait features. Health Technol. 1–14 (2018).
  13. 13.
    Hausdorff, J.M., Cudkowicz, M.E., Firtion, R., Wei, J.Y., Goldberger, A.L.: Gait variability and basal ganglia disorders: stride-to-stride variations of gait cycle timing in Parkinson’s disease and Huntington’s disease. Mov. Disord. 13(3), 428–437 (1998). Scholar
  14. 14.
    Hausdorff, J.M., Lertratanakul, A., Cudkowicz, M.E., Peterson, A.L., Kaliton, D., Goldberger, A.L.: Gait dynamics in neuro-degenerative disease data base.
  15. 15.
    Hausdorff, J.M., Lertratanakul, A., Cudkowicz, M.E., Peterson, A.L., Kaliton, D., Goldberger, A.L.: Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. J. Appl. Physiol. 88(6), 2045–2053 (2000). Scholar
  16. 16.
    Hausdorff, J.M., et al.: Altered fractal dynamics of gait: reduced stride-interval correlations with aging and Huntington’s disease. J. Appl. Physiol. 82(1), 262–269 (1997). Scholar
  17. 17.
    Hausdorff, J.M., Peng, C., Ladin, Z., Wei, J.Y., Goldberger, A.L.: Is walking a random walk? Evidence for long-range correlations in stride interval of human gait. J. Appl. Physiol. 78(1), 349–358 (1995). Scholar
  18. 18.
    Hausdorff, J.M., Purdon, P.L., Peng, C., Ladin, Z., Wei, J.Y., Goldberger, A.L.: Fractal dynamics of human gait: stability of long-range correlations in stride interval fluctuations. J. Appl. Physiol. 80(5), 1448–1457 (1996). Scholar
  19. 19.
    Joshi, D., Khajuria, A., Joshi, P.: An automatic non-invasive method for Parkinson’s disease classification. Comput. Methods Programs Biomed. 145, 135–145 (2017). Scholar
  20. 20.
    Keloth, S.M., Arjunan, S.P., Kumar, D.: Computing the variations in the self-similar properties of the various gait intervals in Parkinson disease patients. In: 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Seogwipo, South Korea, pp. 2434–2437. IEEE, July 2017.
  21. 21.
    Kim, J., Kim, B.S., Savarese, S.: Comparing image classification methods: k-nearest neighbor and support vector machines. Ann Arbor 1001, 48109–2122 (2012)Google Scholar
  22. 22.
    Mitchell, T.M.: Machine Learning, 1st edn. McGraw-Hill Inc., New York (1997)zbMATHGoogle Scholar
  23. 23.
    Peng, C.K., Buldyrev, S.V., Havlin, S., Simons, M., Stanley, H.E., Goldberger, A.L.: Mosaic organization of DNA nucleotides. Phys. Rev. E 49(2), 1685 (1994). Scholar
  24. 24.
    Peng, C.K., Buldyrev, S., Goldberger, A., Havlin, S., Simons, M., Stanley, H.: Finite-size effects on long-range correlations: implications for analyzing dna sequences. Phys. Rev. E 47(5), 3730 (1993). Scholar
  25. 25.
    Quinlan, J.R.: Induction of decision trees. Mach. Learn. 1(1), 81–106 (1986)Google Scholar
  26. 26.
    Rish, I., et al.: An empirical study of the naive Bayes classifier. In: IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, vol. 3, pp. 41–46 (2001)Google Scholar
  27. 27.
    Steinwart, I., Christmann, A.: Support Vector Machines. Springer, New York (2008). Scholar
  28. 28.
    Zeng, W., Wang, C.: Classification of neurodegenerative diseases using gait dynamics via deterministic learning. Inf. Sci. 317, 246–258 (2015). Scholar
  29. 29.
    Zhou, X., Obuchowski, N., McClish, D.: Statistical Methods in Diagnostic Medicine. New York, NY (2002).

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Authors and Affiliations

  1. 1.Instituto de InformáticaUniversidade Federal de GoiásGoiániaBrazil
  2. 2.Escola de Engenharia Elétrica, Mecânica e de ComputaçãoUniversidade Federal de GoiásGoiâniaBrazil

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