# A Low-Cost Video-Based System for Neurodegenerative Disease Detection by Mobility Test Analysis
## 5 Conclusions and Future Work  
In this paper, we proposed a vision-based system for automatically classifying patients with neurodegenerative diseases and healthy subjects while they perform the StS test. Usually, physicians or expert physiotherapists do that by observing people directly. One of the main points of the proposed system was the analysis of real data acquired by a low-cost commercial camera installed in two different retirement houses hosting elderly people. The aim was to support the medical staff in diagnosing neurodegenerative disorders, by using this type of automatic system that is less dependent on human expertise and that can significantly support remote analysis. The use of commercial surveillance cameras for the analysis of the StS test has rarely been used in literature, especially for supporting neurodegenerative disease diagnosis. For this reason, the lack of public datasets and the poor literature coverage in this context did not allow us to make direct comparisons with other works. Nonetheless, the investigation of such a system based on a visual sensor is very important for the development of automatic devices able to monitor the health status of older people in both private homes and nursing institutes. Therefore, in this work, several classical machine learning methodologies, such as SVM, decision tree, neural networks and K-nearest neighbors were applied to the acquired video data to distinguish patients suffering from neurodegenerative diseases and healthy subjects. At the same time, the analysis of the performance of the classifiers over different features allowed us to identify the better one. In general, the obtained good results in terms of classification accuracy, sensitivity and precision encourage us to continue the study and the improvement of the proposed visual system by investigating additional machine learning methodologies. The principal limit to applying more complex machine learning approaches, such as deep ones, remains in the limited dimension of the actual data set. Data augmentation can help manage this issue, but the final size often remains insufficient. On the other side, applying deep neural networks, which are principally characterized by multiple layers in the network, could be very helpful in this context. Deep neural networks can manipulate more abstract representations of the data providing features at higher and higher levels of abstraction. Furthermore, a particular type of deep neural network, known as a recurrent neural network, can extract information from data sequences and is particularly useful for analyzing video streams and learning long-term dependencies of data. In light of these considerations, future work will be devoted to a massive data acquisition phase during physiotherapy sessions of elderly patients for recording videos and collecting the associated physician's evaluations for comparisons. Particular attention will be given to the fundamental issues related to the use of deep learning approaches regarding data volume, data representation and overfitting. Indeed, when a model is trained on a limited dataset, it may learn the peculiarities of the training set and fails to adapt to new data. In conclusion, our future work will regard the acquisition of a substantial quantity of new data in order to study and apply deep learning methods for investigating feature selection, feature reduction and the generalization abilities of the learned models to prevent overfitting problems. Conceptualization, G.C. and T.D. ; methodology, G.C. and T.D. ; software, G.C. ; validation, G.C. and T.D., formal analysis, G.C. and T.D. ; investigation, G.C. ; resources, G.C. and T.D. ; data processing, G.C. ; writing--original draft preparation, G.C. ; writing--review and editing, G.C. and T.D. ; visualization, G.C. ; supervision, T.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable.