## 6 Conclusions  
This study presents an approach to robot-assisted hand motion therapy for stroke patients. The study used machine learning techniques to determine and describe hand motion patterns in healthy individuals. Electromyographic data were collected from the flexion and extension muscles of the forearm using surface electrodes. Feature selection was performed using the Pareto optimization algorithm. The time and frequency characteristics were used as parameters in machine learning algorithms to recognize seven hand gestures and track rehabilitation progress. Research showed that the system was capable of reconstructing hand/finger movement kinematics and simulating the behaviour of every motion pattern. The correlation between the computed joint trajectories and the monitored hand movement was 0.96 on average, and statistical research carried out in various machine learning setups revealed a 92% accuracy in measuring the precision of motion patterns. The experimental study was conducted to determine which statistical estimates of the temporal signal can be used as motion discriminating features and suitable for developing a classification algorithm. The study analyzed nearly 10,500 individual measurements of the specified gestures. The results showed that the highest classification accuracy was obtained for the identification of the 6 th gesture, while the worst accuracy result was obtained for the classification of the second gesture. Gestures 4 and 5 were classified with 75% accuracy. In summary, this study presents a promising approach to robot-assisted hand-motion therapy for stroke patients, which could potentially monitor the progress of rehabilitation or other medical processes. More studies are required to confirm the efficacy of this approach and refine the system for practical use. The study's results showed a high accuracy in measuring hand motion patterns, but the efficacy of this approach must be confirmed in clinical settings with larger patient populations to establish its reliability and effectiveness. Future research should investigate developing personalized therapy plans and adoption of the algorithm to react to a larger variety of potential actions, all based on individual patient needs, rather than a generalized approach, allowing to potentially learn to recognize patterns automatically without the need for manual feature engineering, potentially improving classification accuracy. While the study evaluated the accuracy of the system in measuring hand motion patterns, the long-term effects of robot-assisted therapy on stroke patients' functional recovery and quality of life require further investigation, including the integration of this approach with other therapies, such as occupational and physical therapy. Conceptualization, R.M. ; Data curation, R.M. ; Formal analysis, R.M., R.D., V.R., A.A., J.R. and J.G. ; Investigation, R.M., R.D., V.R., A.A., J.R. and J.G. ; Methodology, R.M. and R.D. ; Supervision, R.M. and J.G. ; Validation, R.M., R.D., A.A. and J.R.; Visualization, R.D. and V.R. ; Writing--original draft, R.M. and R.D. ; Writing--review and editing, R.D. All authors have read and agreed to the published version of the manuscript. This research was funded by EU Structural Funds project financed by the European Regional Development Fund under the 2014-2020 Operational Programme for the Investment of European Union Funds (2014-2020) Measure No 1.2.2-CPVA-K-703 \"Promotion of Centres of Excellence and Centres for Innovation and Technology Transfer\", project number 01.2.2-CPVA-K-703-03-0022. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Number of Motions** & **Number of Subjects** & **Accuracy** \\ \hline Ours & 7 & 25 & 92\% \\ \hline [PERSON] et al. [63] & 8 & 5 & 91.18\% \\ \hline [PERSON] et al. [64] & 6 & 8 & 84.1\% \\ \hline [PERSON] et al. [65] & 12 & 7 & 77.1\% \\ \hline [PERSON] et al. [66] & 6 & 1 & 75\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: A comparison with other related approaches.