## 8 Future Developments  
The currently proposed assisted rehabilitation system represents an experimental prototype, designed to simulate the interaction between the vision and suggestion modules. Several development directions have been outlined aimed at enhancing performance, adaptability, and clinical efficacy. Among the main future evolutions are  
* Continuous real-time machine learning and adaptive personalization;
* Integration with virtual reality (VR) and augmented reality (AR) technologies;
* Multi-user support and application in clinical and healthcare settings;
* Expansion of hardware compatibility and integration with biometric devices;
* Improvement of interoperability with existing healthcare systems. Currently, the suggestion module is based on machine learning models trained on predefined datasets. A key evolution will consist of the implementation of continuous learning techniques, which allow the dynamic updating of predictive models in real time, favoring a greater personalization of therapy. By automatically identifying individual recovery patterns, the system will be able to adapt exercises and levels of assistance based on the patient's progress. In this context, the use of advanced reinforcement learning models will be crucial to automatically calibrate the difficulty of the exercises. Furthermore, the adoption of an architecture based on federated learning will allow us to refine the predictive models by exploiting anonymous data from multiple users, while ensuring compliance with current privacy regulations (e.g., GDPR). The integration of immersive technologies, such as virtual and augmented reality, represents a further step forward in the evolution of the system. VR will allow patients to perform exercises within highly motivating and interactive virtual rehabilitation environments. At the same time, AR will provide real-time visual feedback, superimposing graphic indicators on the patient's movements to facilitate postural correction. The addition of haptic interfaces, capable of returning tactile stimuli, will further enhance the perception and execution of correct movements, increasing the effectiveness of the therapy and patient engagement. From an architectural point of view, the system is currently designed to manage only one patient at a time. Future iterations will need to include optimization for multi-user support, with the possibility of monitoring parallel rehabilitation sessions in complex clinical environments. The introduction of a multi-device interface (accessible from a tablet or computer), together with the integration with telemedicine systems, will allow therapists to remotely manage multiple patients in real time, significantly expanding the application potential of the system. A strategic development concerns the adoption of advanced biometric sensors, which can provide a more complete assessment of the patient's physiological state. These include electromyographic (EMG) sensors to monitor muscle activation, devices for the detection of cardio-respiratory parameters, and intelligent wearables (such as exoskeletons or smart bands) for high-precision postural and biomechanical analysis. The integration of these tools will further improve the customization and precision of therapy. Another evolutionary perspective is represented by the integration of the system with digital clinical platforms, such as electronic health records (EHRs), patient management software, and decision support systems based on artificial intelligence. The development of standardized APIs will facilitate interoperability with IT tools in use at healthcare facilities. Furthermore, the adoption of predictive algorithms based on big data will allow the optimization of treatment planning and recovery time prediction. Automation in the generation of clinical reports will help streamline the work of therapists while increasing the quality of care. The modular architecture of the system facilitates the progressive integration of new features without compromising its stability.