## 5 Conclusions  
In this paper, the feasibility of an objective assessment of the motor symptoms of patients with PD using AI-based video analysis was explored. For this purpose, we collected video clips from regular clinical visits which assessed subjects' status and advancement of their disease; tests were administered by neurologists who assigned scores for each task using the MDS-UPDRS scale. This study focused on alterations affecting motor skills, which are a hallmark of PD, and disorders of balance, posture and gait. Tracking these basic features provides, in most cases, relevant and early clues for the diagnosis and advancement of PD, facilitating the initiation of appropriate treatment strategies and evaluation of their outcome. The results obtained in this pilot study suggest that the proposed system has the potential to provide an objective and accurate assessment of PD without imposing an additional burden on patients, providing clinicians with a range of information with which to deepen their assessments. Our tool not only seems to be correctly capable of recognizing and qualitatively highlighting the same gait impairments assessed by MDS-UPDRS scores but, by also providing a quantitative analysis of the results, can handle a wider and more finely graded range of motor disorders. The system is able to offer an evaluation of the skill of a subject that is not expressed in a range of integer numbers, but that returns to the clinician all the richness of an instrumental measure. The results obtained during this pilot study seem to suggest that it is possible to extend the range in which gait impairment can be expressed. This aspect is decisive both in diagnostics, especially at an early stage of PD, when the symptoms are so mild that they do not reach a score of one on the MDS-UPDRS scale, and in the management of therapy which can thus agree with greater adherence to the real state of the health of the subject. On the other hand, for the freezing of gait, more data should be collected for general consideration, as only one episode occurred across all test sessions. However, it is worth noting that the system was able to correctly detect it, despite it being a minor episode in which the subject briefly froze and then easily started walking again. The system has proved capable of offering clinicians, through accurate and objective data, a second opinion or a suggestion to reconsider the assignment of MDS-UPDRS score. During the test campaign, in one case, a rather evident discrepancy was found between the evaluation carried out by the clinician and the percentage deviations found with respect to the corresponding control subject (nearly 38% shorter average stride length, 37% shorter in both stride width and speed, 84% greater turning time and nearly 32% lower average foot lift height), seeming to suggest the assignment of a different value for the patient. Having brought these data to the attention of the clinician, after a careful evaluation that also included viewing the videos of the tests in which the subject was involved, the clinician decided to change the assigned score, making it consistent with the measures obtained by the proposed system. The combined use of computer vision and deep learning techniques allows the system to obtain helpful information to describe a patient's status and the progress of their disease in a way that does not impact their normal activities. The system is therefore not only effective but also has a low impact on the person, whose abilities, even of a residual type, are evaluated by the system in a way that is as accurate as it is respectful and ergonomic. Thanks to its simplicity, the proposed system can be adopted in higher-level telemedicine on an ongoing basis in environments where people usually live and work as a tactful and non-intrusive approach with clinical-grade accuracy. Conceptualization, A.Z., R.C., N.B.M. and A.S.; methodology, A.Z. and R.C. ; software, A.Z. ; validation, A.Z., R.C., T.S., N.B.M. and A.S.; formal analysis, A.Z. and R.C. ; investigation, A.Z.