Surface EMG signals pattern recognition utilizing an adaptive crosstalk suppression preprocessor

Kianoush Nazarpour

doi:10.1109/CIMA.2005.1662327

Surface EMG signals pattern recognition utilizing an adaptive crosstalk suppression preprocessor

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

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 language	English
Title of host publication	2005 ICSC Congress on Computational Intelligence Methods and Applications
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Number of pages	3
ISBN (Print)	1-4244-0020-1
DOIs	https://doi.org/10.1109/CIMA.2005.1662327
Publication status	Published - 17 Dec 2005
Event	2005 ICSC Congress on Computational Intelligence Methods and Applications - Istanbul, Turkey Duration: 15 Dec 2005 → 17 Dec 2005

Conference

Conference	2005 ICSC Congress on Computational Intelligence Methods and Applications
Country/Territory	Turkey
City	Istanbul
Period	15/12/05 → 17/12/05

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@inproceedings{4c7ef40001f24f3d8e6be799623e71bd,

title = "Surface EMG signals pattern recognition utilizing an adaptive crosstalk suppression preprocessor",

abstract = "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",

author = "Kianoush Nazarpour",

year = "2005",

month = dec,

day = "17",

doi = "10.1109/CIMA.2005.1662327",

language = "English",

isbn = "1-4244-0020-1",

booktitle = "2005 ICSC Congress on Computational Intelligence Methods and Applications",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2005 ICSC Congress on Computational Intelligence Methods and Applications ; Conference date: 15-12-2005 Through 17-12-2005",

}

Nazarpour, K 2005, Surface EMG signals pattern recognition utilizing an adaptive crosstalk suppression preprocessor. in 2005 ICSC Congress on Computational Intelligence Methods and Applications. Institute of Electrical and Electronics Engineers (IEEE), 2005 ICSC Congress on Computational Intelligence Methods and Applications, Istanbul, Turkey, 15/12/05. https://doi.org/10.1109/CIMA.2005.1662327

TY - GEN

T1 - Surface EMG signals pattern recognition utilizing an adaptive crosstalk suppression preprocessor

AU - Nazarpour, Kianoush

PY - 2005/12/17

Y1 - 2005/12/17

N2 - 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

AB - 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

U2 - 10.1109/CIMA.2005.1662327

DO - 10.1109/CIMA.2005.1662327

M3 - Conference contribution

SN - 1-4244-0020-1

BT - 2005 ICSC Congress on Computational Intelligence Methods and Applications

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2005 ICSC Congress on Computational Intelligence Methods and Applications

Y2 - 15 December 2005 through 17 December 2005

ER -

Surface EMG signals pattern recognition utilizing an adaptive crosstalk suppression preprocessor

Abstract

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