## 4 Experimental Setup
### Pattern Classification and Setting  
Following feature extraction from the EMG data, various machine learning algorithms and artificial neural networks (ANNs) are deployed for pattern recognition. The model is trained using the training set's data, and its performance is assessed using the testing dataset. We employ four supervised pattern-recognition algorithms typically used in related hand gesture recognition tasks involving EMG signals [36,37]. These methods encompass linear discriminant analysis (LDA), K-nearest neighbors (KNNs), support vector machine (SVM), and ANN for hand gesture classification.  
Linear discriminant analysis (LDA), k-nearest neighbors (KNNs), and support vector machine (SVM) are widely used machine learning algorithms. LDA, a supervised learning method, aims to identify the optimal linear projection that separates different classes in the data. KNN is an instance-based algorithm that classifies new data points by measuring distances to the nearest K neighbors and making decisions based on their labels. SVM, a powerful binary classifier, seeks to find the best hyperplane that maximizes the margin between classes while minimizing classification errors. In those classifications predication of the test data are evaluated by measuring distance from the training samples. In this work, to determine the class of the testing data Euclidean distance is calculate and the number of neighbors \(K=10\) has been considered. SVM classification consists of a hyper surface in the space that is used for classification of the data set. In our work, RBF (radial basis function) has been used as the SVM kernel. LDA involves projecting multidimensional data features onto one or lower-dimensional space to maximize the distance between different categories while minimizing the distance within the same category.  
In our study, the architecture of our ANN network is structured as follows: it consists of three fully connected layers, where the number of neurons in the hidden layer is dynamically adjusted based on the varying feature quantities. The number of neurons in the output layer is also adapted to the number of categories in different datasets. We have opted for the LeakyReLU activation function [38]. The specific parameter configurations  
Figure 3: A visual juxtaposition of selected signal segments from NinaPro DB5 before and after pre-processing is presented. On the left are the raw signal segments without filtering or normalization, while on the right are the corresponding EMG signal segments post-processing, involving wavelet denoising and min-max normalization.  
are as follows: the learning rate is set at 0.01, with a learning rate adjustment strategy of CosineAnnealingLR. We employ the AdamW optimization function, a batch size of 256, and 200 iterations. To address potential distribution differences between the training and test data, a total trail is used for the training and testing. Specifically, Ninapro DB4, the trials numbered 1, 3, 4, and 6 of all 10 subjects were used for training, and the trials numbered 2 and 5 were used for testing. More detail is provided in Table 1.  
Regarding the hardware setup, we utilized an Intel Core i5-12400F with a 3.9 GHz CPU, 32 GB of RAM, and an Nvidia RTX 3060 GPU. The project was developed using the PyTorch deep learning library, and all comparative experiments were conducted in this aforementioned environment.