## 2 Motion Capture System
### State of the Art in Markerless Motion Capture Systems and Computer Vision for Rehabilitation
[41] used markerless computer vision systems to evaluate the ability to maintain balance while standing on one foot, both with eyes open and with eyes closed. The collected data were then compared with those obtained from a stabilometric platform, the gold standard for analyzing body sway. The experiment utilized a Dynamic Vision Sensor camera capable of detecting changes in pixel-level illumination related to movement. The results showed a high correlation, suggesting that computer vision is a valid tool for balance monitoring in rehabilitation. Home exercises supervised by a therapist are a key component in recovering from conditions such as osteoarthritis or stroke. These exercises aim to reduce joint pain, improve mobility, and reduce therapy costs [42; 43; 44]. Systems that use computer vision to record exercises and provide immediate feedback can significantly improve the effectiveness of rehabilitation. Without feedback, it is difficult to personalize treatment and maintain patient motivation [45]. Performing exercises incorrectly, especially after surgery, can slow or compromise recovery [46]. One example is ArthriKin, a simple and user-friendly system developed by [PERSON] et al. [47], which allows remote supervision by the therapist during home exercise programs. [PERSON] et al. [44] developed a system dedicated to stroke patients, consisting of two connected applications: one for the therapist and one for the patient, who receives real-time visual feedback and detailed reports on exercise performance. Computer vision models have also been developed to track objects to assist patients with motor impairments in reaching and grasping objects, often with the aid of robotic devices. Rehabilitation for patients with arm or wrist injuries has also employed computer vision systems in which a webcam records a cuboid object and software tracks its position in real time within a virtual 3D space [48]. [PERSON] et al. [49] demonstrated how a simple smartphone camera can be used for real-time assessment during vestibular rehabilitation, useful for patients with dizziness symptoms and elderly fall prevention. [PERSON] [50] monitored the movements of people with motor disabilities during therapy using real-time computer vision-based methods [51]. A system for automatic action recognition has also been proposed in upper limb rehabilitation at home. It analyzes sequences of color and depth images to distinguish between correct and incorrect rehabilitation exercises, capturing up to 125 frames per second [52]. [PERSON] et al. [53] developed a markerless system for analyzing manual wheelchair propulsion using two Microsoft Kinect sensors. This system focuses on upper extremity movements during propulsion and uses the open-source platform OpenSim [54] for biomechanical modeling and simulation. Some recent contributions in the field of artificial intelligence propose advanced models based on neural architectures, such as Quantum Transfer Learning [55], pre-trained networks such as ELECTRA [56], and multi-level methodologies for automatic data annotation [57], which also show a notable potential for transferability in the rehabilitation field. Such approaches, in fact, can be adapted to improve the representation, interpretation, and personalization of clinical data, contributing to the development of intelligent systems able to dynamically support therapeutic assistance and monitoring of patient progress. In medicine and physiotherapy, the study and analysis of human movement and rehabilitation exercises using depth cameras is very relevant and promising. In the clinical setting, these new technological devices provide healthcare professionals with precise movement data and objective assessment tools, allowing the creation of personalized and effective treatment plans. Furthermore, this technology allows patients to receive rehabilitation support in real time remotely, overcoming geographical barriers and ensuring simplicity and continuity in therapy.