Lower arm electromyography (EMG) activity detection using local binary patterns

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

21 Citations (Scopus)


This paper presents a new electromyography activity detection technique in which 1-D local binary pattern histograms are used to distinguish between periods of activity and inactivity in myoelectric signals. The algorithm is tested on forearm surface myoelectric signals occurring due to hand gestures. The novel features of the presented method are that: 1) activity detection is performed across multiple channels using few parameters and without the need for majority votemechanisms, 2) there are no per-channel thresholds to be tuned, which makes the process of activity detection easier and simpler to implement and less prone to errors, 3) it is not necessary to measure the properties of the signal during a quiescent period before using the algorithm. The algorithm is compared to other offline single-and double-threshold activity detection methods and, for the data sets tested, it is shown to have a better overall performance with greater tolerance to the noise in the real data set used.

Original languageEnglish
Pages (from-to)1003-1012
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Issue number5
Publication statusPublished - Sept 2014


  • Activity detection
  • electromyography
  • one-dimensional (1-D) local binary patterns
  • onset detection
  • surface electromyography


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