# A Low-Cost Video-Based System for Neurodegenerative Disease Detection by Mobility Test Analysis
## 4 Discussion  
The aim of this work was to investigate the use of a visual system based on a single commercial RGB camera, for distinguishing patients with neurodegenerative diseases from healthy ones by analyzing the StS test. Many neurodegenerative disorders, such as [PERSON]'s or Parkinson's disease, are characterized by motor dysfunction, often culminating in the loss of movement. Therefore, the early detection of neurodegenerative signals, such as motor degeneration, is essential for appropriately supporting both patients and doctors for further clinical investigations.  
The results shown in the previous section clearly prove the effectiveness of the proposed system which represents a valid instrument in this domain. As shown in Tables 2-4, classic machine learning methodologies were able to clearly separate patients suffering from neurodegenerative diseases from healthy subjects reaching high accuracy rates of up to 95.2%. Two types of geometric features were used as input to the machine learning methods: joint-to-joint distances and angles. Furthermore, kinematic features such as the velocity and the acceleration evaluated on both distances and angles were combined with the geometric features in order to investigate their effect on classifiers' performance. In general, the SVM methods outperformed the other classifiers when using as features the distances, the angles and the angles combined with velocity and acceleration (see Tables 2 and 3). In these cases, an accuracy rate of up to 85.7% was achieved. In the case of using distances combined with velocity and acceleration, the method which exhibited better performance was KNN, reaching 85.7% for the accuracy rate (see Table 2).  
These results confirmed that the considered features, both geometric and kinematic, were able to characterize the posture variations of the subjects and to detect motor dysfunctions typical of patients with neurodegenerative diseases. Finally, the last study regarding the combination of the geometric features alone, allowed us to make an additional consideration. Indeed, in this case, the highest percentages of correct classifications were obtained. This emerged not only by considering the accuracy metric, which reached 95.2% but also by considering the sensitivity and precision rates, which achieved percentage values of up to 100%. These results highlighted that was possible to ignore kinematic features since only the geometric ones were enough to characterize the postural variations of the people. At the same time, this result is equally important as it represents a good compromise between classification performance and computational cost.  
Figure 8: Confusion matrices of SVM-Q and NN-N when the combination of normalized distances and angles was used as a feature vector. The diagonal cells show the correctly classified observations (accuracies), whereas the off-diagonal cells show the incorrectly classified ones. The far-right column of each matrix shows the precision or positive predictive value (green) and the false discovery rate (red). The bottom row shows the sensitivity or true positive rate (green) and the false negative rate (red). Class 0 and class 1 represent healthy subjects and sick patients, respectively.