EMG-based Hand Gesture Classification Using Deep Learning

Abstract

In recent years, Deep Learning methods have been successfully applied to a wide range of image and speech recognition problems highly impacting other research fields. As a result, new works in biomedical engineering are directed towards the application of these methods to electromyography-based gesture recognition. In this report, we present a brief overview of Deep Learning methods for electromyography-based hand gesture recognition along with a comparison between different deep learning architectures. We used four architectures that are: CNN, RNN, LSTM and hybrid CNN-LSTM. The proposed networks yield to various levels of accuracy depending on the used model, including that our best model was the CNN model which resulted in the highest accuracy. The proposed analysis helps in understanding the limitations of the model and exploring new ways to improve the performance.