Semg-based hand gesture recognition via dilated convolutional neural networks

Elahe Rahimian, Soheil Zabihi, S. Farokh Atashzar, Amir Asif, Arash Mohammadi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The recent evolution of Artificial Intelligence (AI) and deep learning models coupled with advancements of assistive robotic systems have shown great potential in significantly improving myoelectric control of prosthetic devices. In this regard, the paper proposes a novel deep-learning-based architecture for processing surface Electromyography (sEMG) signals to classify and recognize upper-limb hand gestures via incorporation of dilated causal convolutions. The proposed approach has the potential to significantly improve the overall recognition accuracy due to the specific design of the convolutional layers. By using dilated causal convolutions which gradually increases the receptive field of the network, and by applying Conv1D, the proposed architecture eliminates the need for readjustment of the input sequences and inherently takes into account the hidden temporal correlations existing among the available set of sEMG sequences. Contrary to recent hybrid (RNN-CNN) solutions, the proposed architecture neither uses recurrent units nor 2-dimensional convolutions. The publically-accessible NinaPro DB2 dataset is utilized for training and evaluation of the proposed network. Through an extensive set of experiments, it is observed that the proposed architecture significantly outperforms its state-of-the-art counterparts and provides an average 8.71% performance improvement.

Original languageEnglish (US)
Title of host publicationGlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728127231
DOIs
StatePublished - Nov 2019
Event7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019 - Ottawa, Canada
Duration: Nov 11 2019Nov 14 2019

Publication series

NameGlobalSIP 2019 - 7th IEEE Global Conference on Signal and Information Processing, Proceedings

Conference

Conference7th IEEE Global Conference on Signal and Information Processing, GlobalSIP 2019
CountryCanada
CityOttawa
Period11/11/1911/14/19

Keywords

  • Dilated Causal Convolutions
  • Gesture Recognition
  • Surface Electromyography (sEMG)

ASJC Scopus subject areas

  • Information Systems
  • Information Systems and Management
  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Signal Processing

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