## 3 Results
### AI and ML for Rehabilitation Systems
#### 3.3.1 AI Algorithms and Machine Learning for Personalized Rehabilitation and Recovery Prediction
As reported in other scholarly work, significant advances are being made in the development of intuitive prosthetic control systems using EMG signals and ML methods. Studies conducted on datasets such as GRABMyo demonstrate that advanced signal processing techniques, including filtering and the discrete wavelet transform (DWT), combined with carefully selected feature sets and classification algorithms, such as SVM, can achieve a high gesture recognition accuracy. The quality and diversity of the datasets used play a primary role in ensuring the reliability and broad applicability of the developed models [50]. ML and computational intelligence algorithms have also been applied in the rehabilitation classification and prognosis for reverse total shoulder arthroplasty (RTSA) [50], where comparisons between KNN and GAClust with hybrid computational intelligence approaches revealed that genetic optimization enhances the classification performance while reducing dataset size requirements. In addition, a deep learning model utilizing an IMU sensor successfully classified 11 types of shoulder rehabilitation exercises, achieving a 92.5% test accuracy, as illustrated in Table 3, which demonstrates the feasibility of AI-driven exercise compliance monitoring for personalized therapy [45]. Feature reduction techniques, such as Principal Component Analysis (PCA) and Singular Value Decomposition (SVD), have also improved \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Methods** & **Accuracy** & **Recall** & **Precision** & **F1-Score** & \begin{tabular}{c} **Training/** \\ **Validation** \\ \end{tabular} & **Dataset Composition** & \begin{tabular}{c} **Provenance/** \\ **Accessibility** \\ \end{tabular} & **Reference** \\ \hline Two-Layer & & & & & 1280 tests & \begin{tabular}{c} Ages 8-75, \\ 56\% male/44\% female, \\ post-abdominal surgery \\ \end{tabular} & IEEE DataPort & \\ Neural Network & 90.60\% & - & - & - & - & \begin{tabular}{c} 1280 tests \\ (16 subjects, \\ LOO-CV) \\ \end{tabular} & \begin{tabular}{c} Ages 8-75, \\ 56\% male/44\% female, \\ post-abdominal surgery \\ \end{tabular} & \begin{tabular}{c} IEEE DataPort \\ (public) \\ \end{tabular} & [48] \\ Logistic Regression & 100\% & - & - & - & (70/30) & \begin{tabular}{c} areah \\ (70/30) \\ \end{tabular} & \begin{tabular}{c} Private/Institutional \\ (GDPR, request from \\ \end{tabular} \\ KOOS-ADL functional \\ score & & & & & & \\ \hline \begin{tabular}{c} Linear \\ Discriminant \\ Analysis \\ (LDA)— \\ Support \\ Vector \\ Machine \\ (SVM) \\ Genetic \\ Algorithm- \\ Based \\ Clustering \\ (GAClust) \\ K-Nearest \\ Neighbors \\ (KNN) \\ Adaptive \\ Boosting \\ (AdaBoost) \\ \end{tabular} & 86.98\% & 67.44\% & 67.84\% & 67.48\% & 67.48\% & 645 samples (70/15/15 split), 43 participants & Healthy subjects, mixed gender, 5 gesture types & Public—GRABMyo dataset on PhysioNet & [50] \\ \hline \begin{tabular}{c} Healthy subjects, mixed \\ \end{tabular} & 99\% & 99\% & 99\% & 99\% & 96 training/ 24 test & RTSA rehabilitation metrics & Private/Institutional \\ \hline \begin{tabular}{c} Hyvea/Institutional \\ (physofterap) \\ \end{tabular} & 84\% & 91\% & - & 65\% & 16 training/4 test & \
\begin{tabular}{c} Children, psychological \\ subgroup classification \\ \end{tabular} &
\begin{tabular}{c} Private/Institutional \\ (physofterap) clinic, \\ on request) \\ \end{tabular} & [53] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparative performance analysis of multiple machine learning methods used in rehabilitation. classification accuracy, achieving a 99% accuracy in leg rehabilitation using Random Forest (RF) and KNN, as demonstrated in Table 4[52]. Research on personalized rehabilitation has leveraged multimodal physiological signals and psychological data to identify predictors of effective treatment.