## 2 Related Work
With eight electrodes, they achieved 94.9% accuracy in classifying four different grasp gestures. A good review of the initiatives in this sense can be found in [10], which examined the suitability of upper limb prostheses in the healthcare sector from their technical control perspective. This paper reviewed the overall structure of pattern recognition schemes for myo-controlled prosthetic systems and summarized the existing challenges and future research recommendations. In recent years, this topic has attracted public interest. [PERSON] et al. [17] investigated various ML and Deep Learning (DL) models for sEMG-based gesture classification and found that ensemble learning and deep learning algorithms outperform classical approaches for assistive robotic applications. [PERSON] et al. [18] used four traditional ML models for classifying finger movements across seven distinct gestures. They concluded that the Random Forest emerges as the top performer, consistently delivering superior precision. Real-time performance and low memory consumption are requirements that greatly influence the selection of the technique to be used. In fact, methods such as LDA and kNN cannot meet real-time requirements. Similarly, complex DL networks (CNN, RNN, etc.) cannot meet the memory requirements of low-cost platforms [19]. Among the topics in which greater activity is observed in the research field, the elucidation of how to best adapt the acquired signal to the identification process to which it will be subjected stands out strongly. The problem of determining which characteristics have the greatest influence on signal detection and identification, and how to fuse them for their use through ML techniques, constitutes a notable focus of activity in the literature. In [20], [PERSON] et al. studied a total of 37 time and frequency-domain features extracted from each sEMG channel. Their results highlight the significance of employing an appropriate feature selection method to significantly reduce the number of necessary features while maintaining classification accuracy. In parallel, [PERSON] et al. [21] proposed other sets of features, this being an open question for the time being. On the other hand, [PERSON] et al. [22] have addressed the important question of optimizing sensor placement for accurate hand gesture classification. They also proposed the use of an SVM for solving the problem. In a previous work [19], the authors explored the possibility of using ML algorithms for grasping posture recognition. In that study, a straightforward multi-layer perceptron was designed in MATLAB and deployed on a GPU. The model was optimized to identify nine hand gestures, accounting for 90% of Activities of Daily Living (ADL), in real-time. Achieving an overall success rate of 73% with just two features, the GPU implementation enabled the evaluation of optimal architectures that balanced generalization ability, robustness to electrode displacement, low memory usage, and real-time operation. In this work, the selected algorithm for the classification task is a Support Vector Machine [23] with the aim of increasing the grasping posture recognition accuracy of the previous work [19]. The literature review shows higher recognition accuracies using an SVM than other ML algorithms and also shows that, although there is a remarkable research activity in sEMG classification, many of the previous studies neither focus on real-time implementation on low-cost platforms nor deal with grasping postures. In fact, those that do often use a reduced set of postures are not properly justified and probably insufficient for fully functional prostheses [24]. This work addresses this limitation by considering the most used grasping postures in 90% of ADL, with experimental data obtained offline from the NinaPro public database [25]. This approach aims to provide the prosthesis with a higher level of dexterity, granting the user greater functionality.