# A Low-Cost Video-Based System for Neurodegenerative Disease Detection by Mobility Test Analysis
## 3 Results
### Ablation Study  
An ablation study was carried out considering different combinations of features. In the first set of experiments, normalized distances (\(D_{d,j}\)) and normalized distances combined with velocity (\(D^{\prime}_{d,j}\)) and acceleration (\(D^{\prime\prime}_{d,i}\)) were used as feature vectors provided to all the considered classifiers. Table 2 lists the results obtained in these cases. SVM-L, in case of normalized distances, performed better with respect to the other classifiers, reaching an accuracy of 81% and 84.6% for both precision and sensitivity. However, the high sensitivity of the NN-M was noteworthy. Figure 6 better details these percentages by comparing the confusion matrices of SVM-L and NN-M, respectively. As can be seen, in the bottom row of the matrices, NN-M can better predict patients with neurodegenerative diseases with respect to SVM-L. However, NN-M reveals a deterioration in the correct classification of healthy subjects. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{3}{c}{**Norm. Distances**} & \multicolumn{3}{c}{**Norm. Distances + Vel. + Acc.**} \\ \hline
**Classifier** & **Accuracy** & **Sensitivity** & **Precision** & **Accuracy** & **Sensitivity** & **Precision** \\  & **\%** & **\%** & **\%** & **\%** & **\%** & **\%** \\ \hline DT & 47.6 & 46.2 & 60.0 & 57.1 & 84.6 & 61.1 \\ KNN & 66.7 & 69.2 & 75.0 & **85.7** & **84.6** & **91.7** \\ SVM-L & **81.0** & 84.6 & **84.6** & 81.0 & 76.9 & 90.9 \\ SVM-Q & 76.2 & 84.6 & 78.6 & 81.0 & 76.9 & 90.9 \\ SVM-C & 66.7 & 76.9 & 71.4 & 81.0 & 76.9 & 90.9 \\ NN-N & 61.9 & 84.6 & 64.7 & 76.2 & 69.2 & 90.0 \\ NN-M & 76.2 & **92.3** & 75.0 & 76.2 & 69.2 & 90.0 \\ NN-W & 61.9 & 69.2 & 69.2 & 76.2 & 69.2 & 90.0 \\ \hline \end{tabular}
\end{table} 
Table 2: Classification results when normalized distances (\(D_{d,j}\)) and normalized distances plus velocity (\(D^{\prime}_{d,i}\)) and acceleration (\(D^{\prime\prime}_{d,i}\)) were used as feature vectors. The maximum percentages are highlighted in bold. When the geometric feature of normalized distance (\(D_{d,i}\)) was combined with the kinematic features of velocity (\(D^{\prime}_{d,i}\)) and acceleration (\(D^{\prime\prime}_{d,i}\)), in general, all the considered classifiers performed better than the previous case (see Table 2). The KNN classification technique showed maximum percentages of accuracy (85.7%), sensitivity (84.6%) and precision (91.7%), respectively. However, although a great improvement was obtained, the high dimensionality of the feature vectors introduced a high computational cost. The study continued by considering the angles \(A_{a,i}\) as geometric features and then, the angles combined with the kinematic features of velocity (\(A^{\prime}_{a,i}\)) and acceleration (\(A^{\prime\prime}_{a,i}\)). Table 3 compares the results obtained in these cases. It is evident that the combined use of angles and kinematic features involved an increase in performance with respect to using only the angles, but with a notable growth in computational cost. However, some considerations must be done about the obtained results. To this aim, Figure 7 shows the confusion matrices of two sample cases: SVM-Q on angle features and NN-W on angles plus velocity and acceleration. It is important to notice the sensitivity values in the bottom row of the matrices, which refer to the correct classifications within each category. The case of NN-W on angles, velocity and acceleration provides a very good sensitivity rate for class 0 (healthy) but poor values for class 1 (sick patient), whereas SVM-Q on angles can reasonably classify both classes. In light of the results presented above, to find a good compromise between good classification performance and computational cost, a final study was carried out combining normalized distances (\(D_{d,i}\)) and angles (\(A_{a,i}\)) as a feature vector. As seen in Table 4, all the considered classifiers generally showed good performance with respect to the previous results listed in Tables 2 and 3.