## 9 Conclusions  
The work presented in this study is still in the development and implementation phase. However, the preliminary results and proposed architecture show great potential to bring significant innovations in the field of motor rehabilitation. Thanks to the integration of advanced technologies such as resilient artificial intelligence, computer vision, and deep learning models, the system will be able to support personalized therapies, real-time monitoring, and the dynamic adaptation of rehabilitation protocols in the future. Further development and experimentation will be essential to validate the system's effectiveness and expand its applications, opening new perspectives for digital physiotherapy and improving patients' quality of life. The presented assisted rehabilitation system represents an important step forward in the field of health technologies, combining the use of computer vision, Resilient AI and motor assistance with vision modules to optimize patient rehabilitation.  
The tests carried out on the software prototype have confirmed the validity of the approach, suggesting that the system can significantly improve the effectiveness and personalization of rehabilitation therapies.  
The future potential is vast, including the integration of continuous machine learning to enable the adaptation of real-time therapy to the patient's needs, as well as the use of virtual and augmented reality to enrich the rehabilitation experience.  
The introduction of advanced biometric technologies, such as EMG sensors and devices for cardiac and respiratory monitoring, would represent a further step toward a more complete assessment of the patient's physiological conditions, improving the accuracy of diagnoses and the effectiveness of therapies.  
The possibility of monitoring multiple patients simultaneously and integrating the system with digital clinical platforms such as electronic medical records and patient management software would open up application scenarios in the clinical field, transforming the system into a scalable and easily accessible tool for healthcare professionals.  
Finally, the adoption of modular architectures and the use of technologies such as federated learning could allow for the constant updating of the system, without compromising data privacy and without requiring manual interventions [81].  
Therefore, future developments in the system will therefore contribute to a continuous evolution of rehabilitation therapies, bringing long-term benefits in improving the quality of life of patients and in the efficient management of healthcare resources.  
Conceptualization, E.C.; Methodology, E.C.; Software, E.C.; Validation, E.C. and A.M.; Formal analysis, E.C.; Data curation, E.C., C.C. and M.F.; Writing--original draft, E.C.; Writing--review & editing, E.C.; Visualization, E.C.; Supervision, E.C.; Project administration, E.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study are openly available in the Zenodo repository at [[https://doi.org/10.5281/zenodo.13305826](https://doi.org/10.5281/zenodo.13305826)]([https://doi.org/10.5281/zenodo.13305826](https://doi.org/10.5281/zenodo.13305826)).  
The authors declare no conflict of interest.