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Fig 1.

Illustration of the proposed degenerative neuromuscular disease detection framework.

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Table 1.

Summary of subjects’ description (average ± standard deviation values across subjects).

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Fig 2.

Illustration the stride, stance, and swing phases on the vertical ground reaction force signal marked by the heel-strike and toe-off events.

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Fig 3.

Bilevel waveform estimation of the vertical ground reaction force signal to identify the heel-strike and toe-off actions time points.

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Table 2.

Values of the parameters used for each classification model.

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Fig 4.

Box plot and violin feature distributions for the (a) left and (b) right vertical ground reaction force signal.

The P and chi-square (x2) values on the plots represent the results of the Kruskal-Wallis test. The asterisks represent the pairwise comparison results between disease classes (*:p ≤ 0.05, **: p ≤ 0.01, ***:p ≤ 0.001).

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Fig 4 Expand

Fig 5.

Box plot and violin feature distributions for the (a) left and (b) right stride signal.

The P and chi-square (x2) values on the plots represent the results of the Kruskal-Wallis test. The asterisks represent the pairwise comparison results between disease classes (*:p ≤ 0.05, **: p ≤ 0.01, ***:p ≤ 0.001).

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Fig 5 Expand

Fig 6.

Box plot and violin feature distributions for the (a) left and (b) right stance signal.

The P and chi-square (x2) values on the plots represent the results of the Kruskal-Wallis test. The asterisks represent the pairwise comparison results between disease classes (*:p ≤ 0.05, **: p ≤ 0.01, ***:p ≤ 0.001).

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Fig 6 Expand

Fig 7.

Box plot and violin feature distributions for the (a) left and (b) right swing signal.

The P and chi-square (x2) values on the plots represent the results of the Kruskal-Wallis test. The asterisks represent the pairwise comparison results between disease classes (*:p ≤ 0.05, **: p ≤ 0.01, ***:p ≤ 0.001).

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Fig 7 Expand

Table 3.

Achieved classification performance evaluation metrics for different gait signals using decision trees and different ensemble models.

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Table 3 Expand

Fig 8.

Class-specific evaluation of the best performing AdaBoost ensemble model for the (a) VGRF signal, (b) stride signal, (c) stance signal, and (d) swing signal.

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Table 4.

Comparative summary to state-of-art literature on multi-class classification of neurodegenerative diseases.

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Table 4 Expand