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Deep Learning in EMG-based Gesture Recognition

Book Contribution - Chapter

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.
Book: Proceedings of the 5th International Conference on Physiological Computing Systems - Volume 1: PhyCS
Pages: 107-114
Number of pages: 8
ISBN:978-989-758-329-2
Publication year:2018
  • ORCID: /0000-0001-8042-6834/work/84646395
  • ORCID: /0000-0002-1180-1968/work/71643917
  • ORCID: /0000-0003-3188-2432/work/71404105
  • ORCID: /0000-0002-0688-8173/work/71188385
  • DOI: https://doi.org/10.5220/0006960201070114
Authors:International
Accessibility:Open