Over the past few years, Deep learning (DL) has revolutionized the field of data analysis. Not only are the algorithmic paradigms changed, but also the performance in various classification and prediction tasks has been significantly improved with respect to the state-of-the-art, especially in the area of computer vision. The progress made in computer vision has produced a spillover in many other domains, such as biomedical engineering. Some recent works are directed towards surface electromyography (sEMG) based hand gesture recognition, often addressed as an image classification problem and solved using tools such as Convolutional Neural Networks (CNN). This paper extends our previous work on the application of the Hilbert space-filling curve for the generation of image representations from multi-electrode sEMG signals, by investigating how the Hilbert curve compares to the Peano-and Z-order space-filling curves. The proposed space-filling mapping methods are evaluated on a variety of network architectures and in some cases yield a classification improvement of at least 3%, when used to structure the inputs before feeding them into the original network architectures.
|Number of pages||9|
|Journal||International Journal of Electrical and Computer Engineering Systems|
|Publication status||Published - 21 Apr 2021|
Bibliographical noteFunding Information:
The work is supported by the «Andreas Mentzelo-poulos Scholarships for the University of Patras» and the VUB-UPatras International Joint Research Group on ICT (JICT).
© 2021 J.J. Strossmayer University of Osijek , Faculty of Electrical Engineering, Computer Science and Information Technology. All rights reserved.
Copyright 2021 Elsevier B.V., All rights reserved.
- Deep Learning
- hand gesture recognition
- Hilbert curve
- Peano curve
- space-filling curve
- Z-order curve