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

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

Research output: Contribution to journalArticle

8 Citations (Scopus)

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.
LanguageEnglish
Pages1003-1012
Number of pages10
JournalIEEE Transactions on Neural Systems and Rehabilitation Engineering
Volume22
Issue number5
Early online date29 Apr 2014
DOIs
Publication statusPublished - 1 Sep 2014

Fingerprint

Electromyography
Arm
Gestures
Forearm
Hand
Datasets

Keywords

  • forearm surface myoelectric signals
  • hand gestures
  • lower arm electromyography activity detection
  • myoelectric signal inactivity period classification
  • signal property measurement
  • biomechanics
  • electromyography

Cite this

@article{e27cd59847d64c10a1edce226af912c5,
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.",
keywords = "forearm surface myoelectric signals, hand gestures, lower arm electromyography activity detection, myoelectric signal inactivity period classification, signal property measurement, biomechanics, electromyography",
author = "Paul McCool and Navin Chatlani and Lykourgos Petropoulakis and John Soraghan and Radhika Menon and Heba Lakany",
note = "(c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.",
year = "2014",
month = "9",
day = "1",
doi = "10.1109/TNSRE.2014.2320362",
language = "English",
volume = "22",
pages = "1003--1012",
journal = "IEEE Transactions on Neural Systems and Rehabilitation Engineering",
issn = "1534-4320",
number = "5",

}

Lower arm electromyography (EMG) activity detection using local binary patterns. / McCool, Paul; Chatlani, Navin; Petropoulakis, Lykourgos; Soraghan, John; Menon, Radhika; Lakany, Heba.

In: IEEE Transactions on Neural Systems and Rehabilitation Engineering, Vol. 22, No. 5, 01.09.2014, p. 1003-1012.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Lower arm electromyography (EMG) activity detection using local binary patterns

AU - McCool, Paul

AU - Chatlani, Navin

AU - Petropoulakis, Lykourgos

AU - Soraghan, John

AU - Menon, Radhika

AU - Lakany, Heba

N1 - (c) 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works.

PY - 2014/9/1

Y1 - 2014/9/1

N2 - 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.

AB - 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.

KW - forearm surface myoelectric signals

KW - hand gestures

KW - lower arm electromyography activity detection

KW - myoelectric signal inactivity period classification

KW - signal property measurement

KW - biomechanics

KW - electromyography

UR - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7333

U2 - 10.1109/TNSRE.2014.2320362

DO - 10.1109/TNSRE.2014.2320362

M3 - Article

VL - 22

SP - 1003

EP - 1012

JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering

T2 - IEEE Transactions on Neural Systems and Rehabilitation Engineering

JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering

SN - 1534-4320

IS - 5

ER -