Deep convolutional spiking neural network based hand gesture recognition

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Abstract

Novel technologies for EMG (Electromyogram) based hand gesture recognition have been investigated for many industrial applications. In this paper, a novel approach which is based on a specific designed spiking convolution neural network which is fed by a novel EMG signal energy density map is presented. The experimental results indicate that the new approach not only rapidly decreases the required processing time but also increases the average recognition accuracy to 98.76% based on the Strathclyde dataset and to 98.21% based on the CapgMyo open source dataset. A relative comparison of experimental results between the proposed novel EMG based hand gesture recognition methodology and other similar approaches indicates the superior effectiveness of the new design.
Original languageEnglish
Title of host publication2020 International Joint Conference on Neural Networks (IJCNN)
Place of PublicationPiscataway, NJ.
PublisherIEEE
Number of pages7
ISBN (Electronic)9781728169262
ISBN (Print)9781728169279
DOIs
Publication statusPublished - 28 Sep 2020
Event2020 International Joint Conference on Neural Networks (IJCNN)- IEEE World congress on computational intelligence(WCCI) 2020: IJCNN - glasgow, Glasgow, United Kingdom
Duration: 19 Jul 202024 Jul 2020
https://wcci2020.org/ijcnn-sessions/

Conference

Conference2020 International Joint Conference on Neural Networks (IJCNN)- IEEE World congress on computational intelligence(WCCI) 2020
Abbreviated titleIJCNN
CountryUnited Kingdom
CityGlasgow
Period19/07/2024/07/20
Internet address

Keywords

  • Spiking Neural network
  • Convolutional neural network
  • gesture recognition
  • EMG signal processing

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