title: Lower arm electromyography (EMG) activity detection using local binary patterns.
abstract: 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 vote mechanisms, 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.
authors.forename: Paul
authors.initials: P
authors.lastname: McCool
authors.forename: Navin
authors.initials: N
authors.lastname: Chatlani
authors.forename: Lykourgos
authors.initials: L
authors.lastname: Petropoulakis
authors.forename: John J
authors.initials: JJ
authors.lastname: Soraghan
authors.forename: Radhika
authors.initials: R
authors.lastname: Menon
authors.forename: Heba
authors.initials: H
authors.lastname: Lakany
mesh_terms: D000465:Algorithms; D001132:Arm / Q000502:physiology*; D003627:Data Interpretation, Statistical; D004576:Electromyography / Q000295:instrumentation / Q000379:methods* / Q000706:statistics & numerical data*; D005189:False Positive Reactions; D006225:Hand / Q000502:physiology; D006801:Humans; D009068:Movement / Q000502:physiology