## 2 Review of Related Approaches
Although useful features may be acquired from the large volumes of data created by pooling the signals of several users, reducing the recording effort while improving gesture recognition, there are additional issues, such as bias [46]. [PERSON] et al. [32] compared machine learning and deep learning classifiers on generalization in general across different classes, and offered a systematic examination of the effect of variables in the time domain and pre-processing factors on the performance of the classification model. Ensemble and deep learning algorithms have exceeded other standard machine learning approaches. In [47], EMG data received from the forearm muscles was classified using Recurrent Neural Network (RNN) to improve the online classification of hand motions. This study contrasts and explains a Feed-Forward Neural Network (FFNN), an RNN, a Long Short-Term Memory network (LSTM), and a Gated Recurrent Unit (GRU). The FFNN, LSTM, RNN, and GRU models exhibited equivalent accuracy, 95% for the DualMyo dataset and 91% for the NinaPro DB5 dataset. [PERSON] et al. [48] suggested Convolutional Recurrent Neural Network (CRNN) model to recognize hand motions in real time by combining LSTM for time-series information classification and CNN for feature extraction. Two grips, three hand signals, and one rest are recognized and categorised as hand gestures. For pre-processing, the Short Time Fourier Transform (STFT), Continuous-time Wavelet Transform (CWT), and the recently suggested Scale Average Wavelet Transform (SAWT) are utilized. The CRNN with SAWT and the overlapping window outperformed the other approaches. The recognition of lower-limb motions using the tunable Q-factor wavelet transform (TQWT) and the Kraskov entropy (KrEn) was presented in [49] with a similarly efficient result. [PERSON] et al. [50] proposed to build a gesture classifier using an RNN model that incorporates LSTM units and dense layers, with the objective of reducing the number of EMG channels and total complexity of the model to increase scalability for embedded systems. The model needs only four EMG channels to identify five hand movements, resulting in a considerable decrease in the number of electrodes. The model was trained using a 20 s dataset of gesture EMG signals recorded with a custom EMG armband. During training and validation, the model obtained an accuracy of up to 97%, and an accuracy of 87% during real-time testing. [PERSON] et al. [51] developed an approach to identify hand movements that uses an improved multichannel CNN (IMC-CNN) to extract properties of time and frequency domains from surface electromyography (sEMG). To recognize the 10 most frequently used hand gestures, the spectral features of sEMG signals, which serve as input to the IMC-CNN model, are utilised. The spectrogram characteristics of the sEMG signals from several channels are integrated into a complete enhanced spectrogram feature, which is sent into IMC-CNN to categorize hand motions. The recognition accuracy of the proposed model was 97.5%. In [52], the arm sEMG signal was acquired to infer four types of signals from nine static motions. To reduce redundant data in EMG signals, enhance recognition performance, and allow for real-time recognition, the action recognition system is built using the principal component analysis (PCA) method and the general regression neural network (GRNN). The precise action mode is determined by collecting information from human movements. The total recognition rate of the system was achieved by 95.1% after dimension reduction and model training. [PERSON] et al. [53] demonstrated that the use of recurrent CNN (R-CNN) in terms of EMG signals for hand gesture identification was demonstrated. The signal is captured using electrodes on the forearm, and the features are extracted using the wavelet packet transform. Using the wavelet power spectrum, the R-CNN model achieves 96.48% accuracy. The purpose of the alternative study [54] was to see if movements of the hand and wrist joints affected the recognition of the EMG pattern.