###### Abstract
Due to the high redundancy of time-domain features, dimensionality reduction techniques, the principal component analysis, for example, have been widely applied to improve the results [27]. On the other hand, multiple gesture pattern recognition algorithms based on surface EMG signals have been proposed in previous studies. [PERSON] et al. [31] used a 12-channelsEMG signal and linear discriminant analysis (LDA) to classify 15 different finger movements with 11 time-domain feature values as inputs for 10 non-amputes and 6 sub-elbow amputees with an accuracy of more than 90%. [PERSON] et al. [32] studied the application of the support vector machine (SVM) algorithm in the classification of myoelectric control systems. [PERSON] et al. [27] used 11-channel EMG signals to classify 11 gestures with the help of Independent Component Analysis (ICA) and Icasso clustering with 9 time-domain features values as input with an accuracy of 96.6%. [PERSON] et al. [33] decoded five finger movements of 11 healthy subjects for 16 time-domain feature signals using an artificial neural network (ANN) with an accuracy of over 96%. In the process of gesture pattern recognition, more than nine feature values per EMG signal are frequently used as input to the classifier. The use of more feature values does improve the robustness of the classification, but it also generates high redundancy and increases the computing time of the control system, which in turn leads to a delay in the control system. Therefore, it is important to reduce the number of feature values of the EMG by a dimensionality reduction without reducing the robustness of the classification system. In addition, although a variety of machine learning algorithms had been proposed in previous studies, the three classifiers with the best classification results are k-Nearest Neighbor (k-NN), Support Vector Machines (SVM) and Artificial Neural Networks (ANN). Furthermore, although the results for finger movement classification appear promising, many previous studies had only examined the use of EMG signal feature values as classification inputs and had not considered the male and female muscle differences and sEMG signals on the impact on classification results. Sex-related differences in kinematics [34] and EMG [35] signals were evident in dynamic manual manipulation tasks of the upper limbs. In addition, [PERSON] et al. [36], comparing the similarity in muscle fatigue levels between strength-matched adult males and females, found that EMG activity differed between males and females, with a lower rate of increase in mean-corrected EMG in female muscles compared to males. [PERSON] et al. [37], examining age and gender differences in human skeletal muscle responses during incremental isometric exercise, found that, during exercise, Intracellular concentrations of P\({}_{\text{i}}\) and H\({}_{2}\)PO\({}_{4}\) increased more, and pH decreased more in males compared to females. [PERSON] et al. [38] and [PERSON] et al. [39] also demonstrated sex differences in muscle EMG and muscle oxygenation levels during upper limb exercise, respectively. We speculated that taking sex differences into account in the process of gestural pattern recognition may be a breakthrough in improving the robustness and accuracy of pattern recognition. In this study, two methods through machine learning were proposed to identify and characterize six hand movements in healthy individuals using multichannel sEMG signals, considering sex differences in muscles. For this purpose, the sEMG signals of subjects were analyzed for feature extraction and statistical analysis (right hand muscles in males and females). The k-NN, SVM and ANN classifiers were also applied to compare whether considering sex differences in muscle significantly affects the classification results or not. It was hypothesized that machine learning methods that considered sex differences in muscle would have better classification results and higher average prediction accuracy.