Classification of EMG signals from forearm muscles as automatic control using Naive Bayes

Adi Dwi Irwan Falih, W. Adhi Dharma, Surya Sumpeno

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

9 Citations (Scopus)

Abstract

The wheelchair is still a mobility aids commonly used by patients with muscle weakness or stroke patients. Some stroke patients, having constraints in moving a joystick or controlling an electric wheelchair due to muscle limitations of their hands Myo-armband, as wearable device that have an Electromyogram sensor can be used as an alternative in controlling the electric device like wheelchair more easily. The Electromyography Research (EMG) on feature of particular muscle activation pattern which has correlation with a motion contributes inspiration to be applied as motion control media on electric wheelchair. Classification process of EMG will be a new alternative to control wheelchair movement for user or patient who hasn't latitude to move their limb and just able to do easy motion using their forearm. The stages of this project is detecting signal in the muscle using EMG, extracting feature of muscle response in time domain base, and be classified by Naïve Bayes, the dataset classification is pinned in raspberry and output to arduino controller to be used as output motion in motor of electric wheelchair. The result of this research is classification of MAV feature, Peak number, RMS and Gradient Magnitude in 275 stream of muscle data show that detected and correctly can be discriminate 90.18%, thus, a sum of 248 instances and wrongly 9.8182% a sum of 27 instances.

Original languageEnglish
Title of host publication2017 International Seminar on Intelligent Technology and Its Application
Subtitle of host publicationStrengthening the Link Between University Research and Industry to Support ASEAN Energy Sector, ISITIA 2017 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages346-351
Number of pages6
ISBN (Electronic)9781538627068
DOIs
Publication statusPublished - 28 Nov 2017
Event18th International Seminar on Intelligent Technology and Its Application, ISITIA 2017 - Surabaya, Indonesia
Duration: 28 Aug 201729 Aug 2017

Publication series

Name2017 International Seminar on Intelligent Technology and Its Application: Strengthening the Link Between University Research and Industry to Support ASEAN Energy Sector, ISITIA 2017 - Proceeding
Volume2017-January

Conference

Conference18th International Seminar on Intelligent Technology and Its Application, ISITIA 2017
Country/TerritoryIndonesia
CitySurabaya
Period28/08/1729/08/17

Keywords

  • Arduino
  • Electromyography
  • Forearm muscle
  • Naive Bayes
  • Raspberry

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