< Back to previous page

Publication

A Hilbert curve based representation of sEMG signals for gesture recognition

Book Contribution - Book Chapter Conference Contribution

Deep learning (DL) has transformed the field of data analysis by dramatically improving the state of the art in various classification and prediction tasks, especially in the area of computer vision. In biomedical engineering, a lot of new work is directed towards surface electromyography (sEMG) based gesture recognition, often addressed as an image classification problem using Convolutional Neural Networks (CNN). In this paper, we utilize the Hilbert space-filling curve for the generation of image representations of sEMG signals that are then classified by CNN. The proposed method is evaluated on different network architectures and yields a classification improvement of more than 3%.

Book: Proceedings of IWSSIP 2019 - 2019 International Conference on Systems, Signals and Image Processing
Series: International Conference on Systems, Signals, and Image Processing
Pages: 201-206
Number of pages: 6
ISBN:978-1-7281-3253-2
Publication year:2019
Keywords:Hilbert curve, hand gesture recognition, sEMG, electromyography, classification, CNN, Deep Learning
  • ORCID: /0000-0001-8042-6834/work/84646486
  • ORCID: /0000-0002-1180-1968/work/71644331
  • ORCID: /0000-0003-3188-2432/work/71404108
  • ORCID: /0000-0002-0688-8173/work/71188410
  • Scopus Id: 85070813628
  • DOI: https://doi.org/10.1109/iwssip.2019.8787290
Accessibility:Closed