Deep learning approaches for phantom movement recognition

Akhan Akbulut*, Guven Asci, Feray Gungor, Ela Tarakci, Muhammed Ali Aydin, Abdul Halim Zaim

*Corresponding author for this work

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

1 Citation (Scopus)

Abstract

Phantom limb pain has a negative effect on the life of individuals as a frequent consequence of limb amputation. The movement ability on the lost extremity can still be maintained after the amputation or deafferentation, which is called the phantom movement. The detection of these movements makes sense for cybertherapy and prosthetic control for amputees. In this paper, we employed several deep learning approaches to recognize phantom movements of the three different amputation regions including above-elbow, below-knee and above-knee. We created a dataset that contains 25 healthy and 16 amputee participants' surface electromyography (sEMG) readings via a wearable device with 2-channel EMG sensors. We compared the results of three different deep learning methods, respectively, Multilayer Perceptron, Convolutional Neural Network, and Recurrent Neural Network with the accuracies of two well-known shallow methods, k Nearest Neighbor and Random Forest. Our experiments indicate, Convolutional Neural Network-based model achieved an accuracy of 74.48% in recognizing phantom movements of amputees.

Original languageEnglish
Title of host publicationTIPTEKNO 2019 - Tip Teknolojileri Kongresi
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728124209
DOIs
Publication statusPublished - Oct 2019
Externally publishedYes
Event2019 Medical Technologies Congress, TIPTEKNO 2019 - Izmir, Turkey
Duration: 3 Oct 20195 Oct 2019

Publication series

NameTIPTEKNO 2019 - Tip Teknolojileri Kongresi

Conference

Conference2019 Medical Technologies Congress, TIPTEKNO 2019
Country/TerritoryTurkey
CityIzmir
Period3/10/195/10/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

Keywords

  • Deep Learning
  • EMG
  • Movement Recognition
  • Phantom Limb Pain
  • Phantom Movement

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