Convolutional neural network proposal for wrist position classification from electromyography signals

Alvaro D. Orjuela-Canon, Oscar J. Perdomo-Charry, Cesar H. Valencia-Nino, Leonardo Forero

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

Abstract

Commonly, electromyography (EMG) signals have been employed for movements or pattern classification. For this, different digital signals processing methods are applied to extract features, before a classification stage. The present work deals with a proposal based on the use of image classification employing deep learning techniques. The images were obtained from a spectrogram analysis as a previous process of the convolutional neural network employment. Then, a classification of five positions from wrist movements is carried out the model. Results showed that the accuracy is comparable to similar techniques employed with a shallow neural network and a deep neural network applied to the same dataset.

Original languageEnglish (US)
Title of host publication2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings
EditorsAlvaro David Orjuela-Canon
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728194066
DOIs
StatePublished - Aug 7 2020
Event2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Virtual, Cali, Colombia
Duration: Aug 7 2020Aug 9 2020

Publication series

Name2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings

Conference

Conference2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020
Country/TerritoryColombia
CityVirtual, Cali
Period8/7/208/9/20

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Science Applications
  • Computer Vision and Pattern Recognition
  • Decision Sciences (miscellaneous)
  • Information Systems and Management
  • Control and Optimization

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