sEMG Signal

Surface electromyography (sEMG) measures electrical activity in muscles via skin-surface electrodes, primarily aiming to decode movement intent and muscle force. Current research heavily focuses on improving signal quality through advanced denoising techniques, often employing neural networks like convolutional neural networks (CNNs), transformers, and recurrent neural networks (RNNs), as well as exploring novel signal processing methods like score-based diffusion models and Riemannian manifold analysis. These advancements are crucial for enhancing the accuracy and reliability of sEMG-based applications, impacting fields such as prosthetic control, human-computer interaction, and ergonomic assessment.