Surface EMG signals pattern recognition utilizing an adaptive crosstalk suppression preprocessor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

This paper proposes utilization of a least mean square (LMS) based finite impulse response (FIR) adaptive filter block, before conventional surface electromyogram (sEMG) signal pattern classification schemes. This novel configuration suppresses the sEMG between channels crosstalk. In this study, the sEMG signals are detected from the biceps and triceps brachii muscles to identify four primitive motions, i.e., elbow flexion/extension and forearm supination/pronation. A multi layer perceptron (MLP) classifies the two time domain feature vectors that are extracted from raw and preprocessed sEMG signals, respectively. Although the implementation of an adaptive filter increases computational complexity, significant advances in sEMG pattern classification has been achieved
Original languageEnglish
Title of host publication2005 ICSC Congress on Computational Intelligence Methods and Applications
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Number of pages3
ISBN (Print)1-4244-0020-1
DOIs
Publication statusPublished - 17 Dec 2005
Event2005 ICSC Congress on Computational Intelligence Methods and Applications - Istanbul, Turkey
Duration: 15 Dec 200517 Dec 2005

Conference

Conference2005 ICSC Congress on Computational Intelligence Methods and Applications
Country/TerritoryTurkey
CityIstanbul
Period15/12/0517/12/05

Fingerprint

Dive into the research topics of 'Surface EMG signals pattern recognition utilizing an adaptive crosstalk suppression preprocessor'. Together they form a unique fingerprint.

Cite this