## 2 Motion Capture System
### State of the Art in Markerless Motion Capture Systems and Computer Vision for Rehabilitation  
This state-of-the-art review explores the main approaches to markerless Motion Capture, focusing on advances in computer vision and AI technologies for human motion analysis. Motion Capture is an important technique for biomechanics, used to diagnose problems related to musculoskeletal diseases [18; 19] and to develop rehabilitation therapies [20]. However, its use in clinical settings is still limited due to high costs and long processing times [21]. Markerless systems represent a significant technological advancement as they allow motion analysis without the need to apply sensors to the body, reducing preparation time and increasing efficiency. Markerless systems are divided into active and passive systems. Active markerless systems emit visible or infrared light, such as lasers or light pulses, to detect movement, providing very precise 3D measurements, but require static measurements and controlled environments, such as laser scanning of the human body [22; 23]. More interest is focused on passive systems that do not emit signals and allow motion to be captured in more natural environments. This technology is based on computer vision and AI and is used in various fields, such as surveillance, virtual reality, animation, and the study of human movement [24]. The techniques employed vary depending on the number of cameras and algorithms, which can be model-based or model-free. Model-based approaches rely on a predefined anatomical model to track the human body, using representations such as stick figures [25], cylinders [26], superquadrics [27], or CAD models [28]. Model-free approaches estimate motion without a fixed model, for example, by exploiting bounding boxes [29] or analyzing body transformations through the medial axis [30]. Several studies have attempted to classify Motion Capture methods [31]. [PERSON] et al. [23] analyzed more than 130 research studies published between 1980 and 2000, while [PERSON] et al. [22] proposed a classification system based on image processing stages. However, many of these studies focus on surveillance and use simplified models of the human body, often with only one camera, making motion analysis less accurate. For applications in biomechanics and medicine, it is crucial to accurately measure the 3D movement of the joints. This is challenging because each person has a different body shape, movements are complex, and body parts sometimes occlude others in camera views. Using multiple cameras helps solve this problem and makes the analysis more accurate. Many studies have developed computer vision methods to analyze human movement, introducing techniques such as simulated annealing to improve posture prediction from markers [32], systems to estimate joint centers [33], methods to track lower limb movement [34], analysis of motor impairments, and evaluation of working postures [35]. [PERSON] [34] developed a markerless method to track the lower extremities, while [PERSON] et al. [36] used a camera to analyze the contours of the body and match them to a superquadric model. [PERSON] et al. [37] improved the system using three cameras, articulated 3D models, and fuzzy clustering techniques [38]. The use of markerless motion analysis systems based on computer vision allows assessments to be conducted directly in the patient's home environment. Often, the need to travel to clinical facilities represents a significant barrier to accessing therapy [39]. Real-time monitoring systems based on computer vision have already been developed and tested to support remote rehabilitation. Balance control plays a crucial role, especially in elderly individuals, as falls can cause serious injuries. Although most fall-related injuries are mild, between 5% and 10% of cases in people over 65 are severe [40]. To identify people at risk of falling, [PERSON] et al.