Table 1.
Summary of the neurodegenerative diseases (NDDs) gait classification literatures.
| Literature | Summary of the Classification Algorithm | ||
|---|---|---|---|
| Feature Extraction | Classifier | Cross-Validation | |
| [18] | Radial basis function (RBF) neural networks | RBF neural networks | All training all testing and LOOCV |
| [19] | Mean, standard deviation, max, min, skewness, kurtosis, Lempel-Ziv complexity, fuzzy entropy and Teager–Kaiser energy feature | Support vector machine (SVM), random forest (RandF), multilayer perceptron (MLP) and k-nearest neighbor (KNN) | LOOCV |
| [20] | shifted 1D-LBP | Bayes Network (BayesNT), naïve Bayes (NB), logistic regression (LR), MLP, Partial C4.5 decision tree (PART), RandF and functional tree (FT) | 10-fold cross-validation |
| [21] | Approximate entropy (ApEn), normalized symbolic entropy (NSE), signal turns count (STC) | Generalized linear regression analysis (GLRA) and SVM | LOOCV |
| [22] | Dual channel LSTM | Dual channel LSTM | LOOCV |
| [23] | Discrete wavelet transform (DWT) | Linear discriminant analysis (LDA) and NBC | All training all testing and LOOCV |
| [24] | Fuzzy recurrence plot (FRP) + Gray-level co-occurrence matrix (GLCM) | Least squares support vector machine (LS-SVM) and LDA | LOOCV |