Abstract
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.
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Paula Felix, J., Henrique Teles Vieira, F., Augusto Pereira Franco, R., Martins da Costa, R., Lopes Salvini, R. (2019). Diagnosing Huntington’s Disease Through Gait Dynamics. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11845. Springer, Cham. https://doi.org/10.1007/978-3-030-33723-0_41
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