Authors:
P. Tsinganos
1
;
B. Cornelis
2
;
J. Cornelis
2
;
B. Jansen
2
and
A. Skodras
1
Affiliations:
1
University of Patras, Department of Electrical and Computer Engineering, 26504 Patras and Greece
;
2
Vrije Universiteit Brussel, Department of Electronics and Informatics, 1050 Brussels and Belgium
Keyword(s):
sEMG, Gesture Recognition, Deep Learning, CNN.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Biosignal Acquisition, Analysis and Processing
;
Computational Intelligence
;
Data Manipulation
;
Health Engineering and Technology Applications
;
Human-Computer Interaction
;
Methodologies and Methods
;
Neural Networks
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Theory and Methods
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 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.