## 2 Literature Review
The sensitivity as well as specificity while categorizing various compensatory activities are represented here by the receiver operating characteristic (ROC) curve and the area under the ROC curve in the individual cases. For the binary classification, \(\alpha\), \(\beta\), and Y are the angles while performing good and poor compensatory postures. So, the mentioned study concludes that the TSP dataset is reasonable to use in place of using marker-based movement capture systems. In this baseline process, the algorithms identify the relation between compensatory activities and the angles between them. Thus, comparatively better sensitivity and specificity can be obtained from the ML algorithms while working with the compensatory postures. The very first study regarding marker-free vision-based rehabilitation therapy was proposed by [PERSON] [11] in 2017 which automatically detects compensatory movements of stroke survivors. The Toronto Rehabilitation Institute (TRI) developed a robotic prototype to pave the way for shoulder and elbow postures during rehabilitation therapy. This system identifies the accuracy of the performance of the compensatory activities. Moreover, this system detects abnormal movements due to muscle weakness, incorrect postural adjustments, and so on. Each patient sat in front of the robotic equipment and repeated the movements five times. Compensatory motions are categorized manually and multiclass classifiers are trained depending on the orientation of the three-dimensional segments. The Hidden Markov SVM classifier attained 86% accuracy. The main objective of the mentioned work is to detect common compensatory movements of healthy persons with a classifier. As the levels are classified into four classes, a multiclass classifier is required. A filter of a window size of 31 frames is used here to remove unwanted additional noise effects. Then, the Kinect-centric coordinates are converted to real-world coordinates through translation and rotation. The spine shoulder joint position is considered the reference position with a (0,0,0) 3D coordinate system. SVM as well as recurrent neural network (RNN) classifiers are used to train the various movements of the classifiers. Leave-one-participant-out cross-validation (LOPOCV) detects classifier performance as well as hyperparameter tuning. Though a multiclass RF classifier and Softmax classifier are applied in the mentioned study, they cannot provide superior outcomes. Moreover, the classifiers are trained and tested with the healthy participants first and then with the stroke survivors. The ROC curve and the F1 score represent the performance matrices. Micro-average, macro-average, and the area under the ROC curve are indicated in the plots to detect each type of movement. LF compensations are detected with an SVM that shows a pretty high accuracy, with a 98% AUC and 82% F1 score, whereas TR compensation indicates a 77% AUC and a 57% F1 score. The other two classifiers SE and RNN have quite similar accuracy values, but the moderate values of the SVM and RNN are much better. F1 score represents better the performance of classification. LF achieved outstanding performance, followed by TR and SE. Among all of previous studies conducted with the TRSP dataset, most of them used SVM or RNN classifiers. Moreover, one of them used deep learning and the rest of them applied various ML classifiers. Upper limb compensatory movement detection and vision-based gesture detection have been carried out in previous works. Analyzing all of the previous works, we have focused on a more robust Kinect-based automatic rehabilitation system with a feedback mechanism. In our work, we are going to detect compensatory postures with real-time monitoring and a better fitted model with better performance.