## 2 Review of Related Approaches  
The EMG-based gesture recognition process comprises classifying the unknown observed data into the most likely gesture that corresponds to the target movements [27]. Current research on EMG-based hand gesture categorization encounters challenges, such as poor classification accuracy, low generalization ability, a scarcity of training data, and a lack of robustness [28]. Recent research has focused on developing user-independent interfaces based on gesture recognition to diminish the time of calibration for novice users [29]. However, because EMG signals are erratic, signal analysis performance is generally poor [30,31]. Furthermore, due to the presence of noise and the need for significant processing power, it is very difficult to employ sEMG signals as user control signals [32]. For example, [PERSON] et al. [29] offered a user-independent gesture identification system based on sensor fusion technology that combines the data from IMU and EMG sensors. The Myo Armband was employed to evaluate muscle activity and mobility of healthy participants. The data acquired from 22 people were used to classify the movements, and the Adaptive Least-Squares Support Vector Machine (SVM) model was 92.9% accurate. Similarly, [PERSON] et al. [33] provided a real-time approach to recognise five movements on the right hand. These are the exact actions that the Myo armband's patented technology detects. A shallow artificial feed-forward neural network is used in the proposed system. The correlations among the EMG channels and the result of a bag of five components given to each EMG channel are sent into this network. [PERSON] et al. [34] offered a mixed feature approach strategy for EMG classification that classifies signals acquired from a single channel device using handmade features generated from temporal-spectral analysis and deep features produced from a convolutional neural network (CNN). The approach requires only 100 impulses out of each gesture for training, significantly reducing the time necessary to train the system. Such techniques suggested combine hand-crafted characteristics from a time-spectral investigation with deep features to build the feature vector [35]. Subsequently, the EMG characteristic vector is often classified using a multilayer perceptron classifier (MLPC) [36]. In [37], EMG-based hand/finger motion classifiers were developed based on fixed electrode placement. Three channels of MG signals were monitored and each channel yielded six time-domain (TD) characteristics. A total of 18 attributes were used to create subject-specific classifiers for 10 gestures using an artificial neural network (ANN) and several machine learning classifiers. ANN had the highest mean accuracy and the lowest intersubject variability in accuracy, showing that subject-specific variance in EMGdata had low influence on it. The research [38] proposed BoostEMD, a unique version of [PERSON]'s Empirical Mode Decomposition (EMD) approach for computing higher-order intra-mode functions (IMFs) that identify higher frequency oscillations in EMG signals. The authors demonstrated the efficacy of denoising by classifying the EMG data before and after denoising, as well as analysing the characteristics of the recovered noise signal, similarly to [39]. [PERSON] et al. [40; 41] proposed using the beta artificial bee colony (BetaABC) and binary BetaABC (BBABC) to pick important features in the recognition of the EMG pattern to improve the classification performance while reducing the complexity of the classifier, where the EMG signal was decomposed and the characteristics were extracted using the discrete wavelet transform (DWT). Deep learning methods are a very popular use case for the classification of gestures using EMG signals [42; 43]. However, deep learning algorithms are rarely used in the field of EMG-based gesture detection, as they take an unacceptable amount of effort from a single individual [44; 45].