Surface Electromyography

Surface electromyography (sEMG) measures electrical activity in muscles via surface electrodes, primarily aiming to decode muscle activation patterns for various applications. Current research heavily focuses on improving the accuracy and robustness of sEMG-based gesture recognition and force prediction using deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs, including LSTMs and GRUs), and transformers, often coupled with techniques like attention mechanisms and feature selection. This work holds significant implications for prosthetic control, rehabilitation robotics, human-computer interaction, and clinical diagnostics by providing a non-invasive method for monitoring muscle activity and movement intent.