Surface EMG classification for prosthesis control: Fuzzy logic vs. artificial neural network

Siti Anom Ahmad, Mohd Asyraf Khalid, Asnor J. Ishak, Sawal Hamid Md Ali

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

1 Citation (Scopus)

Abstract

Electromyography control system (ECS) is a well-known technique for prosthesis control application. It consists of two main modules namely feature extraction and classification. This paper presents the investigation of the classification module in the ECS. The surface electromyographic (EMG) signals were recorded from flexor and extensor muscles of the forearm during wrist flexion and extension. Standard deviation and mean absolute value were used to extract information from the raw EMG signals. Two different classifiers, fuzzy logic and artificial neural network were used in investigating the surface EMG signals. The classifier is responsible to determine the movement of the subject's limb during specific moment. The two classifiers were compared in terms of their performance.

Original languageEnglish
Title of host publicationBIOSIGNALS 2012 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing
Pages317-320
Number of pages4
Publication statusPublished - 2012
EventInternational Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2012 - Vilamoura, Algarve
Duration: 1 Feb 20124 Feb 2012

Other

OtherInternational Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2012
CityVilamoura, Algarve
Period1/2/124/2/12

Fingerprint

Fuzzy logic
Electromyography
Classifiers
Neural networks
Control systems
Muscle
Feature extraction
Prostheses and Implants

Keywords

  • Artificial neural network
  • Classification
  • Electromyography
  • Fuzzy logic
  • Prosthesis control

ASJC Scopus subject areas

  • Signal Processing

Cite this

Ahmad, S. A., Khalid, M. A., Ishak, A. J., & Md Ali, S. H. (2012). Surface EMG classification for prosthesis control: Fuzzy logic vs. artificial neural network. In BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing (pp. 317-320)

Surface EMG classification for prosthesis control : Fuzzy logic vs. artificial neural network. / Ahmad, Siti Anom; Khalid, Mohd Asyraf; Ishak, Asnor J.; Md Ali, Sawal Hamid.

BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing. 2012. p. 317-320.

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

Ahmad, SA, Khalid, MA, Ishak, AJ & Md Ali, SH 2012, Surface EMG classification for prosthesis control: Fuzzy logic vs. artificial neural network. in BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing. pp. 317-320, International Conference on Bio-inspired Systems and Signal Processing, BIOSIGNALS 2012, Vilamoura, Algarve, 1/2/12.
Ahmad SA, Khalid MA, Ishak AJ, Md Ali SH. Surface EMG classification for prosthesis control: Fuzzy logic vs. artificial neural network. In BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing. 2012. p. 317-320
Ahmad, Siti Anom ; Khalid, Mohd Asyraf ; Ishak, Asnor J. ; Md Ali, Sawal Hamid. / Surface EMG classification for prosthesis control : Fuzzy logic vs. artificial neural network. BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing. 2012. pp. 317-320
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