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 J.
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 - 2014/9/30
Y1 - 2014/9/30
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
SN - 1534-4320
VL - 22
SP - 1003
EP - 1012
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
IS - 5
ER -
