1-D local binary patterns for onset detection of myoelectric signals

Paul McCool, Navin Chatlani, Lykourgos Petropoulakis, John Soraghan, Radhika Menon, Heba Lakany

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

11 Citations (Scopus)

Abstract

This paper presents a new 1-D LBP (Local Binary Pattern) based technique for onset detection. The algorithm is tested on forearm surface myoelectric signals that occur due to lower arm gestures. Unlike other onset detection algorithms, the method does not require manual threshold setting and fine-tuning, which makes it faster and easier to
implement. The only variables are window size, histogram type and the number of histogram bins. It is also not necessary to measure the properties of the signal during a
quiescent period before the algorithm can be used. 1-D LBP Onset Detection is compared with single and double threshold methods and is shown to be more robust and accurate.
Original languageEnglish
Title of host publication2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO)
PublisherIEEE
Pages499-503
Number of pages5
ISBN (Print)978-1-4673-1068-0
Publication statusPublished - 28 Aug 2012
Event20th European Signal Processing Conference - Bukarest, Romania
Duration: 27 Sep 20121 Oct 2012

Conference

Conference20th European Signal Processing Conference
CountryRomania
CityBukarest
Period27/09/121/10/12

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Keywords

  • signal processing
  • prosthesis
  • local binary patterns
  • detection
  • myoelectric signals
  • myoelectric signals
  • surface electromyography
  • manual threshold setting
  • histogram

Cite this

McCool, P., Chatlani, N., Petropoulakis, L., Soraghan, J., Menon, R., & Lakany, H. (2012). 1-D local binary patterns for onset detection of myoelectric signals. In 2012 Proceedings of the 20th European Signal Processing Conference (EUSIPCO) (pp. 499-503). IEEE.