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 paper, we present a brief overview of Deep Learning methods for electromyography-based hand gesture recognition along with an analysis of a modified simple model based on Convolutional Neural Networks. The proposed network yields a 3% improvement on the classification accuracy of the basic model, whereas the analysis helps in understanding the limitations of the model and exploring new ways to improve the performance.
Original languageEnglish
Title of host publicationProceedings of the 5th International Conference on Physiological Computing Systems - Volume 1: PhyCS
Number of pages8
ISBN (Print)978-989-758-329-2
Publication statusPublished - 27 Sep 2018

ID: 44819480