Fig 1.
Description of experimental set-up and methodological approach.
A schematic description of the walking and artificial neural network method used to map time-distance and kinematic features on the H&Y (1, 2, 3 and 4) levels.
Table 1.
Kinematic parameters.
Fig 2.
Accuracy of artificial neural networks and the best mean confusion matrix.
For diagnosis (A) and staging (B), in the first row the accuracy of artificial neural networks and in the second row the best mean confusion matrixes considering all PCA features as INPUT (a1 and b1) and subset of 2 features (knee RoM and trunk rotation RoM (a2)) and subsets of 4 features (walking speed, hip, knee and ankle RoMs (b2); walking speed, hip, knee and trunk rotation RoMs (b3)). Six different architectures of neural networks were represented by varying the numbers of hidden layers (1, 2, or 3) and the numbers of neurons in each hidden layer based on the numbers of nodes N in the first hidden layer.
Table 2.
Sensitivity and specificity.
Table 3.
Ability to discriminate between PwPD and HS and between disability levels.