DocumentCode :
1851092
Title :
1-D Local Binary Patterns for onset detection of myoelectric signals
Author :
McCool, Paul ; Chatlani, Navin ; Petropoulakis, Lykourgos ; Soraghan, John J. ; Menon, Radhika ; Lakany, Heba
Author_Institution :
Centre for Excellence in Signal & Image Process., Univ. of Strathclyde, Glasgow, UK
fYear :
2012
fDate :
27-31 Aug. 2012
Firstpage :
499
Lastpage :
503
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.
Keywords :
electromyography; medical signal detection; 1D LBP; 1D local binary pattern; fine-tuning; forearm surface myoelectric signal onset detection algorithm; histogram bin type; lower arm gestur; manual threshold setting; signal measurement; window size; Detection algorithms; Educational institutions; Histograms; Manuals; Muscles; Standards; Synchronous motors; 1-D Local Binary Patterns; onset detection; surface electromyography;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European
Conference_Location :
Bucharest
ISSN :
2219-5491
Print_ISBN :
978-1-4673-1068-0
Type :
conf
Filename :
6334024
Link To Document :
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