LSTM Classification of sEMG Signals For Individual Finger Movements Using Low Cost Wearable Sensor

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

63 Downloads (Pure)

Abstract

The electrical activity of the muscles that control finger movements can be extracted during the performance of these movements and using machine learning techniques, the myoelectric signals can be decoded and classified according to the movement that generated the specific signal. The focus of this paper is to classify sEMG signal using easily accessible cheap hardware to capture the signal. Furthermore, to employ neural networks to classify the signal using established methodology i.e. feature extraction, with the highest possible accuracy. To classify these sEMG signals, an LSTM network has been developed and was able to classify 12 individual finger movements with accuracies reaching 90%.
Original languageEnglish
Title of host publication2020 International Symposium on Community-Centric Systems, CcS 2020
PublisherIEEE
Pages1-8
Number of pages8
ISBN (Electronic)978-1-7281-8741-9
ISBN (Print)978-1-7281-8742-6
DOIs
Publication statusPublished - 20 Oct 2020

Publication series

Name2020 International Symposium on Community-Centric Systems, CcS 2020

Keywords

  • Finger Movement Classification
  • LSTM
  • Myo
  • sEMG

Fingerprint Dive into the research topics of 'LSTM Classification of sEMG Signals For Individual Finger Movements Using Low Cost Wearable Sensor'. Together they form a unique fingerprint.

Cite this