Classification of Hand Movements Using DNN, LSTM, and SNN: A Comparative Study

The goal of this project is to classify four distinct hand movements using machine learning techniques applied to data generated from high-density surface EMG (HD- sEMG) signals. We will evaluate three types of neural networks: Deep Neural Networks (DNN), Long Short-Term Memory Networks (LSTM), and Spiking Neural Networks (SNN). Each method will test for its ability to accurately identify movements associated with four types of isometric contractions flexion, extension, pronation, and supination performed at 10% of MVC. This study will highlights the comparative analysis in the context of movement recognition.