# A Low-Cost Video-Based System for Neurodegenerative Disease Detection by Mobility Test Analysis
## 3 Results  
As described in Section 2.2, during the data acquisition phase, 41 videos were acquired: 32 videos of patients with neurodegenerative diseases and 19 videos of healthy subjects. The dataset was divided into a training set and a testing set by randomly considering 60% of the videos for training and the remaining 40% for testing. The random choice of the sample videos in the two sets was maintained fixed for all the experiments in order to be sure to compare fairly the performance of classifiers on the same elements. Furthermore, the training set was oversampled by applying the SMOTE procedure. The following machine learning methods were used to classify patients with neurodegenerative diseases and healthy subjects:  
* K-nearest neighbors with \(K=1\) (KNN)
* Decision tree (DT)
* Support vector machines with linear kernel (SVM-L), with quadratic kernel (SVM-Q) and with cubic kernel (SVM-C)
* Feedforward neural networks with Relu activation functions and different architecture configurations: one fully connected hidden layer of size 10 (NN-N), of size 25 (NN-M) and of size 100 (NN-W).  
The classifiers were trained with a five-fold cross-validation technique. The popular metrics of accuracy, sensitivity and precision (see Table 1) were used for measuring the performance of the obtained classification models.  
In Table 1, _TP_ = true positives, _TN_ = true negatives, _FP_ = false positives and _FN_ = false negatives. Notice that a positive prediction (output class = 1) classified the subject as affected with neurodegenerative disease, whereas a negative prediction (output class = 0) referred to a healthy subject. Accuracy provides general information on how correct the classification model is overall. However, when dealing with binary classification problems, especially in medical contexts, as in our study, it is important to have information about predictions classified as false positives and false negatives. Thus, sensitivity and precision better characterize model performance relative to a specific category. Indeed, precision or positive predictive value provides the percentage of relevant results, whereas sensitivity gives the percentage of relevant results that are successfully classified.  
\begin{table}
\begin{tabular}{c} \hline \hline
**Metrics Definition** \\ \hline Accuracy = \(\frac{TP+TN}{TP+FN+FP+TN}\) \\ Sensitivity = \(\frac{TP}{TP+FN}\) \\ Precision = \(\frac{TP}{TP+FP}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Definition of metrics used to compare the classification models.  
Different experimental sessions were carried out in order to find the best compromise between computational cost and classifiers' performance. Due to the great heterogeneity of people participating in the experiment because of different people's characteristics such as age, disease severity, body size and motor abilities, the variance in times of execution of the StS was considerably high. Therefore, the acquired videos had different lengths in terms of the number of frames. The length of the videos of healthy subjects varied between 206 and 532 frames, whereas that relative to the videos of patients affected by neurodegenerative diseases varied between 233 and 825 frames. Thus, considering the whole video, the dimension of the input feature vector could be very high, producing high computational costs. To tackle this issue, we decided to reduce the number of frames to be processed by defining a step parameter \(s\). Thus, we process one frame every \(s\) in the video. Different values for \(s\) (\(s=2\), \(s=5\), \(s=10\)) were fixed considering the minimum and maximum lengths of the videos of the entire dataset and the camera frame rate. In the following sections, the results obtained for the case of \(s=10\) are shown. This case represents the best one considering both computational cost and feature vector dimension. Additional experiments carried out by using values of \(s>10\) produced a degradation of the classifiers' performance.