Abstract
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%.
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Twelves, D., Perkins, K.S., Counsell, C.: Systematic review of incidence studies of Parkinson’s disease. Mov. Disord. 18(1), 19–31 (2003)
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
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
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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 2006
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
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 2014
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 2016
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)
Acknowledgments
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.
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Bortone, I. et al. (2017). A Novel Approach in Combination of 3D Gait Analysis Data for Aiding Clinical Decision-Making in Patients with Parkinson’s Disease. In: Huang, DS., Jo, KH., Figueroa-García, J. (eds) Intelligent Computing Theories and Application. ICIC 2017. Lecture Notes in Computer Science(), vol 10362. Springer, Cham. https://doi.org/10.1007/978-3-319-63312-1_44
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DOI: https://doi.org/10.1007/978-3-319-63312-1_44
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