## 2 Motion Capture System
### State of the Art in Markerless Motion Capture Systems and Computer Vision for Rehabilitation
[70] automated the evaluation of exercises using advanced neural networks, making rehabilitation training more efficient. [PERSON] et al. [71] estimated the patient's center of mass using data augmentation techniques, improving training and balance. [PERSON] et al. [72] used RGB video to accurately estimate joint positions during rehabilitation exercises. Finally, [PERSON] et al. [73] created virtual skeletal representations to assess patients' ability to perform functional movements, making home rehabilitation easier. Computer vision employs various algorithms to process images and videos of patients performing physiotherapy and rehabilitative exercises. These algorithms, through machine learning, enable the analysis of movements, the evaluation of exercise correctness, and the personalization of the recovery process. This leads to more precise and effective treatments, with the added benefit of providing real-time feedback and adapting to each patient's specific needs. Traditional machine learning algorithms have been widely used in many early studies. Among them are Random Forest (RF) [74], Logistic Regression (LR) [75], Support Vector Machine (SVM) [76], and principal component analysis (PCA) [77], which are valued for their interpretability and computational efficiency. [PERSON] et al. [69] applied RF, LR, and LSTM to estimate human posture in structured datasets concerning lower limb movement, achieving excellent results, with RF reaching 99.8% accuracy. [PERSON] et al. [64] combined PCA, NLPCA, and LR to analyze movement strategies in patients suffering from lower back pain [31]. In a comparative study, [PERSON] et al. [67] evaluated several machine learning techniques using a feature set that included posture, posture derivatives, and dynamic characteristics. In this analysis, the multilayer perceptron (MLP) achieved the highest classification accuracy at 52.3%, followed by RF at 51.8% and SVM at 47.2%. The results highlighted that the expansion and diverse combination of extracted features significantly improved the performance of all analyzed algorithms, emphasizing the importance of a wide variety of features to achieve more accurate classifications. With the rapid progress of deep learning techniques, numerous studies have adopted deep neural network models for the analysis of patients' rehabilitation data, such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short-Term Memory (LSTM) networks, which have proven effective in managing complex and repetitive data in the form of time series, typical of habitual bodily movements. [PERSON] et al. [58] highlighted the potential of CNNs in posture and human movement analysis tasks, achieving accuracies ranging from 60.7% to 93.8%. In a study conducted by [PERSON] et al. [63], a hybrid deep learning (HDL) model was developed, combining CNNs, RNNs, and CNN-GRU architectures for recognizing upper limb rehabilitation movements. The results show that the CNN model alone achieved 98% accuracy, while the hybrid CNN-LSTM and CNN-GRU architectures achieved 99% and 100% accuracy, respectively. [PERSON] et al. [71] proposed an innovative approach integrating CNNs and RF classifiers to estimate the center of mass (CoM) of the human body. This hybrid system showed excellent performance, with sensitivity and specificity values above 80% for four-level classification and over 90% for binary classification. These deep learning models have the advantage of automatically extracting complex features directly from raw data, significantly reducing manual work in feature selection and improving generalization capabilities, making them particularly suitable for processing high-dimensional and large-scale visual data. Recent studies have adopted algorithm fusion strategies to combine the strengths of different methods and enhance the accuracy of pathological movement analysis.