## 2 Related Work  
The analysis of surface electromyography (sEMG) signals is inherently complex due to their stochastic nature [7]. Numerous factors significantly impact the characteristics of sEMG signals in the context of myoelectric control for upper-limb prostheses [8]. Key challenges include temporal variations in signal properties, electrode displacement, muscle fatigue, inter-subject differences, fluctuations in muscle contraction intensity, and changes in limb position or forearm orientation [9; 10; 11]. In pattern-recognition-based myoelectric control, the interface between the user and the prosthesis is a classifier. This classifier is an algorithm trained to recognize the movements the patient intends to perform by analyzing patterns in the sEMG signals. Through this control strategy, the classifier maps sEMG signal patterns to their corresponding movements and controls the prosthesis motors to replicate them. This approach can be used for the simultaneous control of multiple degrees of freedom (DOF), which would significantly reduce the cognitive load required to operate the prosthesis. To effectively address the intricate and variable nature of sEMG signals in advanced applications, a substantial volume of data is often required. Existing techniques face limitations in enabling simultaneous and proportional control of multiple degrees of freedom (DOF) [10]. Many of them have not yet been adequately tested in practical or clinical settings. As a result, achieving control simplicity and maintaining low costs are critical objectives in the development of prosthetic hands. Among the existing machine-learning (ML) techniques, Support Vector Machine (SVM) is a supervised ML method commonly used for sEMG signal classification. Although originally a binary classifier, distinguishing between two classes, SVM can be extended to multi-class classification by combining multiple SVMs. SVM has attracted significant attention due to its superior performance in EMG data classification compared to other methods. [PERSON] et al. [12] conducted a comparison of different ML algorithms for sEMG signal classification to predict knee joint angles. They compared k-Nearest Neighbor (kNN), Linear Discriminant Analysis (LDA), Naive Bayes (NB), and Support Vector Machine (SVM) with various kernels. The results showed that SVM with a quadratic kernel performed best, achieving an accuracy of 93.07 \(\pm\) 3.84%. They used EMG signals from four different muscles and extracted 15 features from both the time and frequency domains for classification. [PERSON] and [PERSON] [13] acquired multi-channel EMG signals to recognize fifteen different finger movements (constituting five individual finger movements and ten combined finger movements) with 16 features, comparing NN, LDA, and SVM. Additionally, they employed two feature selection algorithms: Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO). The results demonstrated that a naive Bayes classifier with ACO showed an average classification accuracy of 88.89% using features such as MAV, ZC, SSC, and WL (definitions of these features can be found in Appendix A for completeness). SVM has also been applied in the diagnosis of muscular dystrophy disorders. [PERSON] and [PERSON] [14] achieved 95% accuracy using an SVM with a polynomial kernel to detect this disorder. They pre-processed the signal using wavelet-based decomposition techniques. [PERSON] [15] designed a low-cost mechatronics platform for the development of robotic hands controlled via sEMG. Using this platform, they compared several classification algorithms, including ANN, LDA, and SVM. By employing temporal features such as MAV, SSV, WL, ZC, and fourth-order AR coefficients with normalized data, they achieved 92.8% accuracy. [PERSON] and [PERSON] [16] used the Myo armband from Thalmic Labs, the same device used in this project, to acquire sEMG signals from the forearm and classify hand gestures using an SVM with a linear kernel.