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, Volume 78, Issue 14, pp 19697–19734 | Cite as

Statistical methods for analysis of Parkinson’s disease gait pattern and classification

  • Anup NandyEmail author
Article
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Abstract

Understanding the human gait and extracting intrinsic feature helps to classify walking patterns of Parkinson disease patients. The measurement of time series gait pattern is required to detect gait disturbances observed in medical gait data. An attempt is taken to compute Normalized Auto Correlation (NAC) along the temporal axis which calculates the degree of gait fluctuation in control subjects (CO) and Parkinson patient’s (PD) gait. In this paper, an underlying statistical analysis is addressed to understand the statistical nature of data. Identifying the proper distribution of these data in advance discards the unwanted information which helps to preserve more informative features. Four different normality testing methods (i.e. W/S, Kolmogorov-Smirnov, Shapiro Wilk and Anderson Darling) are applied to ensure whether the acquired gait data are modelled by a normal distribution. It precludes the costly error during feature analysis to produce the accurate results. A feature selection method, Fisher Discriminant Ratio (FDR) is applied to select most discriminative feature among all the statistical features (i.e. Mean, Median, Mode, Standard Deviation, Variance, Skewness and Kurtosis) derived from both the classes. A probabilistic classifier based on Bayes’ theorem demonstrates its efficiency in the classification of Parkinson gait with illustrating statistical error metrics (i.e. MAE, RMSE, MCE, MSE, SEM, SSE etc.).

Keywords

Human gait Normalized auto correlation Parkinson disease Statistical normality testing Fisher discriminant ratio Probabilistic classifier Error metrics 

Notes

Acknowledgments

We are extremely thankful to PhysioNet people for sharing their medical gait data with us for completing this research work. I would also extend my thanks to pervious B.Tech. Students, Monika and Kamnee, Upharika and Nurzin in my previous Institute for their contribution to this research work.

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Computer Science and Engineering Department, Machine Intelligence and Bio-Motion Research LaboratoryNational Institute of Technology, RourkelaRourkelaIndia

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