A Novel Approach in Combination of 3D Gait Analysis Data for Aiding Clinical Decision-Making in Patients with Parkinson’s Disease

  • Ilaria Bortone
  • Gianpaolo Francesco Trotta
  • Antonio Brunetti
  • Giacomo Donato Cascarano
  • Claudio Loconsole
  • Nadia Agnello
  • Alberto Argentiero
  • Giuseppe Nicolardi
  • Antonio Frisoli
  • Vitoantonio Bevilacqua
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10362)


The most common methods used by neurologist to evaluate Parkinson’s Disease (PD) patients are rating scales, that are affected by subjective and non-repeatable observations. Since several research studies have revealed that walking is a sensitive indicator

for the progression of PD. In this paper, we propose an innovative set of features derived from three-dimensional Gait Analysis in order to classify motor signs of motor impairment in PD and differentiate PD patients from healthy subjects or patients suffering from other neurological diseases. We consider kinematic data from Gait Analysis as Gait Variables Score (GVS), Gait Profile Score (GPS) and spatio-temporal data for all enrolled patients. We then carry out experiments evaluating the extracted features using an Artificial Neural Network (ANN) classifier. The obtained results are promising with the best classifier score accuracy equal to 95.05%.


Parkinson’s disease Gait analysis Artificial neural network Classification 



This work was partially supported by the Italian Ministry of Education University and Research under the Framework “Social Innovation” (DD 84 Ric, March 2nd 2012) with the Grant PON04a3_00097.


  1. 1.
    Twelves, D., Perkins, K.S., Counsell, C.: Systematic review of incidence studies of Parkinson’s disease. Mov. Disord. 18(1), 19–31 (2003)CrossRefGoogle Scholar
  2. 2.
    Bevilacqua, V., Nuzzolese, N., Barone, D., Pantaleo, M., Suma, M., D’Ambruoso, D., Volpe, A., Loconsole, C., Stroppa, F. Fall detection in indoor environment with kinect sensor. In: INISTA 2014 – Proceedings of the IEEE International Symposium on Innovations in Intelligent Systems and Applications, pp. 319–324 (2014). doi: 10.1109/INISTA.2014.6873638
  3. 3.
    Magdalinou, N., Morris, Huw R.: Clinical features and differential diagnosis of parkinson’s disease. In: Falup-Pecurariu, C., Ferreira, J., Martinez-Martin, P., Chaudhuri, K.R. (eds.) Movement Disorders Curricula, pp. 103–115. Springer, Vienna (2017). doi: 10.1007/978-3-7091-1628-9_11 CrossRefGoogle Scholar
  4. 4.
    Song, J., Fisher, B.E., Petzinger, G., Wu, A., Gordon, J., Salem, G.J.: The relationships between the unified Parkinson’s disease rating scale and lower extremity functional performance in persons with early-stage Parkinson’s disease. Neurorehabilit. Neural Repair 23(7), 657–661 (2009)CrossRefGoogle Scholar
  5. 5.
    Patel, S., Chen, B.R., Mancinelli, C., Paganoni, S., Shih, L., Welsh, M., Dy, J., Bonato, P.: Longitudinal monitoring of patients with Parkinson’s disease via wearable sensor technology in the home setting. In: Engineering in Medicine and Biology Society, EMBC, 2011 Annual International Conference of the IEEE, pp. 1552–1555. IEEE (2011)Google Scholar
  6. 6.
    Esser, P., Dawes, H., Collett, J., Feltham, M.G., Howells, K.: Assessment of spatio–temporal gait parameters using inertial measurement units in neurological populations. Gait Posture 34(4), 558–560 (2011)CrossRefGoogle Scholar
  7. 7.
    Morris, M.E., Huxham, F., McGinley, J., Dodd, K., Iansek, R.: The biomechanics and motor control of gait in Parkinson disease. Clin. Biomech. 16(6), 459–470 (2001)CrossRefGoogle Scholar
  8. 8.
    Blin, O., Ferrandez, A.M., Serratrice, G.: Quantitative analysis of gait in Parkinson patients: increased variability of stride length. J. Neurol. Sci. 98(1), 91–97 (1990)CrossRefGoogle Scholar
  9. 9.
    Lewis, G.N., Byblow, W.D., Walt, S.E.: Stride length regulation in Parkinson’s disease: the use of extrinsic, visual cues. Brain 123(10), 2077–2090 (2000)CrossRefGoogle Scholar
  10. 10.
    Bloem, B.R., Valkenburg, V.V., Slabbekoorn, M., Willemsen, M.D.: The Multiple Tasks Test: development and normal strategies. Gait Posture 14(3), 191202 (2001)CrossRefGoogle Scholar
  11. 11.
    Morris, M., Iansek, R., McGinley, J., Matyas, T., Huxham, F.: Threedimensional gait biomechanics in Parkinson’s disease: Evidence for a centrally mediated amplitude regulation disorder. Mov. Disord. 20(1), 40–50 (2005)CrossRefGoogle Scholar
  12. 12.
    Delval, A., Salleron, J., Bourriez, J.L., Bleuse, S., Moreau, C., Krystkowiak, P., Defebvre, L., Devos, P., Duhamel, A.: Kinematic angular parameters in PD: reliability of joint angle curves and comparison with healthy subjects. Gait Posture 28(3), 495501 (2008)CrossRefGoogle Scholar
  13. 13.
    Davis, R.B., Ounpuu, S., Tyburski, D., Gage, J.R.: A gait analysis data collection and reduction technique. Hum. Mov. Sci. 10(5), 575–587 (1991)CrossRefGoogle Scholar
  14. 14.
    Baker, R., McGinley, J.L., Schwartz, M.H., Beynon, S., Rozumalski, A., Graham, H.K., Tirosh, O.: The gait profile score and movement analysis profile. Gait Posture 30(3), 265–269 (2009)CrossRefGoogle Scholar
  15. 15.
    Schutte, L.M., Narayanan, U., Stout, J.L., Selber, P., Gage, J.R., Schwartz, M.H.: An index for quantifying deviations from normal gait. Gait Posture 11(1), 25–31 (2000)CrossRefGoogle Scholar
  16. 16.
    Baker, R., McGinley, J.L., Schwartz, M., Thomason, P., Rodda, J., Graham, H.K.: The minimal clinically important difference for the Gait Profile Score. Gait Posture 35(4), 612–615 (2012)CrossRefGoogle Scholar
  17. 17.
    Mazurowski, M.A., Habas, P.A., Zurada, J.M., Lo, J.Y., Baker, J.A., Tourassi, G.D.: Training neural network classifiers for medical decision making: The effects of imbalanced datasets on classification performance. Neural Netw. 21(2), 427–436 (2008)CrossRefGoogle Scholar
  18. 18.
    Bevilacqua, V., Mastronardi, G., Menolascina, F., Pannarale, P., Pedone, A.: A novel multi-objective genetic algorithm approach to artificial neural network topology optimization: the breast cancer classification problem. In: International Joint Conference on Neural Networks, IJCNN 2006, pp. 1958–1965. IEEE, July 2006Google Scholar
  19. 19.
    Bevilacqua, V., Pacelli, V., Saladino, S.: A novel multi objective genetic algorithm for the portfolio optimization. In: Huang, D.-S., Gan, Y., Bevilacqua, V., Figueroa, J.C. (eds.) ICIC 2011. LNCS, vol. 6838, pp. 186–193. Springer, Heidelberg (2011). doi: 10.1007/978-3-642-24728-6_25 CrossRefGoogle Scholar
  20. 20.
    Bevilacqua, V., Tattoli, G., Buongiorno, D., Loconsole, C., Leonardis, D., Barsotti, M., Frisoli A., Bergamasco, M.: A novel BCI-SSVEP based approach for control of walking in virtual environment using a convolutional neural network. In: 2014 International Joint Conference on Neural Networks (IJCNN), pp. 4121–4128. IEEE, July 2014Google Scholar
  21. 21.
    Bevilacqua, V., Brunetti, A., Triggiani, M., Magaletti, D., Telegrafo, M., Moschetta, M.: An optimized feed-forward artificial neural network topology to support radiologists in breast lesions classification. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, pp. 1385–1392. ACM, July 2016Google Scholar
  22. 22.
    Bradley, A.P.: The use of the area under the ROC curve in the evaluation of machine learning algorithms. Pattern Recogn. 30(7), 1145–1159 (1997)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Ilaria Bortone
    • 1
  • Gianpaolo Francesco Trotta
    • 2
  • Antonio Brunetti
    • 3
  • Giacomo Donato Cascarano
    • 3
  • Claudio Loconsole
    • 3
  • Nadia Agnello
    • 4
  • Alberto Argentiero
    • 4
  • Giuseppe Nicolardi
    • 5
  • Antonio Frisoli
    • 1
  • Vitoantonio Bevilacqua
    • 3
  1. 1.PERCRO LaboratoryTeCIP Institute Scuola Superiore Sant’AnnaPisaItaly
  2. 2.Department of Mechanics, Mathematics and Management EngineeringPolytechnic University of BariBariItaly
  3. 3.Department of Electrical and Information EngineeringPolytechnic University of BariBariItaly
  4. 4.ISBEM S.C.p.ABrindisiItaly
  5. 5.Laboratory of Human Anatomy and Neuroscience, Department of Biological and Environmental Technologies and SciencesUniversity of SalentoLecceItaly

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