ABSTRACT
Panagiotis Tsinganos,a Athanassios Skodras,a Bruno Cornelis,b
Over the past years, Deep Learning methods have shown promis-
ing results to a wide range of research fields including image
classification and natural language processing. Their increased
success rates have drawn the attention of many researchers
from various domains. This chapter investigates the application of
Deep Learning methods to the problem of electromyography-based
gesture recognition. A signal processing pipeline based on Deep
Learning is presented through examples taken from the literature,
whereas the details of state-of-the-art neural network architectures
are discussed. In addition, this chapter illustrates a few ways
adopted from image classification tasks that visualize what the
neural network learns. Finally, new approaches are proposed and
evaluated with publicly available datasets.
