## 6 Conclusions and Future Work  
This work presents a machine-learning technique for myoelectric hand prostheses based on the recognition of grasp patterns using sEMG signals and SVM algorithms optimized with Bayesian techniques. The results obtained demonstrate an average accuracy of 85% in the classification of nine grasp types that represent 90% of the activities of daily living, highlighting the use of features in the time domain to improve computational efficiency and reduce processing times. These findings suggest that the use of accessible acquisition devices, such as the Myo armband, can provide effective and affordable solutions for monitoring low-cost prostheses.  
Despite the promising results, this study has certain limitations. For example, the low separation capacity between some grasp types, such as \"cylindrical grasp\" and \"hook grasp\", reduces the overall accuracy of the model. In future works, additional features and more advanced algorithms could be explored to improve classification. Furthermore, the integration of haptic feedback could be another important step towards a more intuitive user experience for prosthetic users.  
This study is based on the analysis of previously collected offline data (the NinaPro Database), enabling a detailed evaluation of the proposed system's capabilities. Real-time implementation on low-cost platforms will be tackled in the near future. Such investigations will involve the evaluation of new features, such as sparse domains and model-based approaches, as well as the development of a real-time classification system using low-cost hardware platforms. In this way, the practical feasibility of the system in everyday applications will be validated.  
Conceptualization, A.G. and M.C.M.; methodology, M.C.M. and J.V.G.O.; software, A.G. and M.C.M.; validation, A.G. and M.C.M.; formal analysis, M.C.M. and J.C.-B.; investigation, M.C.M., J.V.G.O. and J.C.-B.; resources, M.C.M., J.V.G.O. and J.C.-B.; data curation, M.C.M.; writing--original draft preparation, A.G., M.C.M. and J.C.-B.; writing--review and editing, M.C.M., J.C.-B. and J.V.G.O.; visualization, M.C.M. and J.V.G.O.; project administration, M.C.M.; funding acquisition, M.C.M., J.V.G.O. and J.C.-B. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Spanish Ministry of Economy, Industry and Competitiveness (Grant no: PID2020-118021 RB-I00/AEI/10.13039/501100011033) and Universitat Jaume I (Grant no: UJI-B2022-48).  
The study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Universitat Jaume I (protocol code CD/37/2022 UNIQUE-HAND).  
Not applicable.  
The data presented in this study belong to the NinaPro Database 5 and are openly available at [[https://ninapro.hevs.ch/instructions/DB5.html](https://ninapro.hevs.ch/instructions/DB5.html)]([https://ninapro.hevs.ch/instructions/DB5.html](https://ninapro.hevs.ch/instructions/DB5.html)) and at [[https://zenodo.org/record/1000116#Xo4](https://zenodo.org/record/1000116#Xo4) vZsgZPY]([https://zenodo.org/record/1000116#Xo4](https://zenodo.org/record/1000116#Xo4) vZsgZPY) (accessed on 2 December 2024).  
**Conflicts of Interest:** The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.