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Surface electromyography (sEMG) has illustrated noteworthy findings over different disciplines; however, it suffers from several issues like signal interference, noise, and interruptions. In this research, the Savitzky-Golay filter was first used to extract meaningful data while preserving the overall shape of the data, and then two hybrid neural network models based on Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) were performed to predict the missing sEMG data. Their performance was compared with independent LSTM and GRU models using the coefficient of determination (R-squared), Root Mean Square Error (RMSE), and correlation coefficient (ρ). All models were trained, validated, and tested on extended and limited datasets. In addition, the optimal number of hidden neurons was determined experimentally for each condition. The outcomes indicated that the deep learning architecture based on sequential GRU and LSTM models outperformed all competitors with a prediction accuracy of 99.25% and 98.91% for the long and short training datasets, respectively. [ABSTRACT FROM AUTHOR]

Feature selection is a fundamental aspect of machine learning that is crucial for improving the accuracy and efficiency of models. It carefully analyzes the abundance of data to identify the most significant characteristics, hence improving the accuracy of predictions and minimizing the likelihood of model overfitting. This technique not only optimizes model training by reducing computational requirements, but also enhances the model's interpretability, resulting in more transparent and reliable predictions. The deliberate omission of unnecessary variables is a process of improving the model and also constitutes a crucial measure toward achieving more flexible and comprehensible results in machine learning. An analysis to assess the effectiveness of feature selection on regression models was conducted. The impact was measured using Mean Squared Error (MSE) metrics. A variety of regression algorithms were evaluated, and then feature selection techniques, including statistical and algorithmic methods, such as SelectKBest, PCA, and RFE with Linear Regression and Random Forest, were applied. After selecting the features, linear models demonstrated improvements in mean squared error (MSE), highlighting the value of removing unnecessary data. This study emphasizes the subtle impact of feature selection on model performance, calling for a tailored strategy to maximize prediction accuracy. [ABSTRACT FROM AUTHOR]