EMG based classification of basic hand movements based on time frequency features

Christos Sapsanis; George Georgoulas; Anthony Tzes

doi:10.1109/MED.2013.6608802

EMG based classification of basic hand movements based on time frequency features

Christos Sapsanis, George Georgoulas, Anthony Tzes

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

Abstract

This paper proposes an integrated approach for the identification of daily hand movements with a view to control prosthetic members. The raw EMG signal is decomposed into Intrinsic Mode Functions (IMFs) with the use of Empirical Mode Decomposition (EMD). A number of features are extracted in time and in frequency domain. Two different dimentionality methods are tested, namely the Principal Component Analysis (PCA) technique and the RELIEF feature selection algorithm. The outputs of the dimensionality reduction stage are then fed to a linear classifier to perform the detection task. The approach was tested on a group of young individuals and the results appear promising.

Original language	English (US)
Title of host publication	2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings
Pages	716-722
Number of pages	7
DOIs	https://doi.org/10.1109/MED.2013.6608802
State	Published - 2013
Event	2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Platanias-Chania, Crete, Greece Duration: Jun 25 2013 → Jun 28 2013

Publication series

Name	2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings

Other

Other	2013 21st Mediterranean Conference on Control and Automation, MED 2013
Country/Territory	Greece
City	Platanias-Chania, Crete
Period	6/25/13 → 6/28/13

Keywords

Biomedical signal analysis
Electromyography
Empirical Mode Decomposition
Pattern classification
Principal Component analysis
RELIEF feature selection

ASJC Scopus subject areas

Artificial Intelligence
Control and Systems Engineering

Access to Document

10.1109/MED.2013.6608802

Cite this

Sapsanis, C., Georgoulas, G., & Tzes, A. (2013). EMG based classification of basic hand movements based on time frequency features. In 2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings (pp. 716-722). [6608802] (2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings). https://doi.org/10.1109/MED.2013.6608802

EMG based classification of basic hand movements based on time frequency features. / Sapsanis, Christos; Georgoulas, George; Tzes, Anthony.

2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings. 2013. p. 716-722 6608802 (2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings).

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

Sapsanis, C, Georgoulas, G & Tzes, A 2013, EMG based classification of basic hand movements based on time frequency features. in 2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings., 6608802, 2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings, pp. 716-722, 2013 21st Mediterranean Conference on Control and Automation, MED 2013, Platanias-Chania, Crete, Greece, 6/25/13. https://doi.org/10.1109/MED.2013.6608802

Sapsanis C, Georgoulas G, Tzes A. EMG based classification of basic hand movements based on time frequency features. In 2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings. 2013. p. 716-722. 6608802. (2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings). doi: 10.1109/MED.2013.6608802

@inproceedings{7f81a814198d4bd19cc843ba619d13ab,

title = "EMG based classification of basic hand movements based on time frequency features",

abstract = "This paper proposes an integrated approach for the identification of daily hand movements with a view to control prosthetic members. The raw EMG signal is decomposed into Intrinsic Mode Functions (IMFs) with the use of Empirical Mode Decomposition (EMD). A number of features are extracted in time and in frequency domain. Two different dimentionality methods are tested, namely the Principal Component Analysis (PCA) technique and the RELIEF feature selection algorithm. The outputs of the dimensionality reduction stage are then fed to a linear classifier to perform the detection task. The approach was tested on a group of young individuals and the results appear promising.",

keywords = "Biomedical signal analysis, Electromyography, Empirical Mode Decomposition, Pattern classification, Principal Component analysis, RELIEF feature selection",

author = "Christos Sapsanis and George Georgoulas and Anthony Tzes",

year = "2013",

doi = "10.1109/MED.2013.6608802",

language = "English (US)",

isbn = "9781479909971",

series = "2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings",

pages = "716--722",

booktitle = "2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings",

note = "2013 21st Mediterranean Conference on Control and Automation, MED 2013 ; Conference date: 25-06-2013 Through 28-06-2013",

}

TY - GEN

T1 - EMG based classification of basic hand movements based on time frequency features

AU - Sapsanis, Christos

AU - Georgoulas, George

AU - Tzes, Anthony

PY - 2013

Y1 - 2013

N2 - This paper proposes an integrated approach for the identification of daily hand movements with a view to control prosthetic members. The raw EMG signal is decomposed into Intrinsic Mode Functions (IMFs) with the use of Empirical Mode Decomposition (EMD). A number of features are extracted in time and in frequency domain. Two different dimentionality methods are tested, namely the Principal Component Analysis (PCA) technique and the RELIEF feature selection algorithm. The outputs of the dimensionality reduction stage are then fed to a linear classifier to perform the detection task. The approach was tested on a group of young individuals and the results appear promising.

AB - This paper proposes an integrated approach for the identification of daily hand movements with a view to control prosthetic members. The raw EMG signal is decomposed into Intrinsic Mode Functions (IMFs) with the use of Empirical Mode Decomposition (EMD). A number of features are extracted in time and in frequency domain. Two different dimentionality methods are tested, namely the Principal Component Analysis (PCA) technique and the RELIEF feature selection algorithm. The outputs of the dimensionality reduction stage are then fed to a linear classifier to perform the detection task. The approach was tested on a group of young individuals and the results appear promising.

KW - Biomedical signal analysis

KW - Electromyography

KW - Empirical Mode Decomposition

KW - Pattern classification

KW - Principal Component analysis

KW - RELIEF feature selection

UR - http://www.scopus.com/inward/record.url?scp=84885202015&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84885202015&partnerID=8YFLogxK

U2 - 10.1109/MED.2013.6608802

DO - 10.1109/MED.2013.6608802

M3 - Conference contribution

AN - SCOPUS:84885202015

SN - 9781479909971

T3 - 2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings

SP - 716

EP - 722

BT - 2013 21st Mediterranean Conference on Control and Automation, MED 2013 - Conference Proceedings

T2 - 2013 21st Mediterranean Conference on Control and Automation, MED 2013

Y2 - 25 June 2013 through 28 June 2013

ER -

EMG based classification of basic hand movements based on time frequency features

Abstract

Publication series

Other

Keywords

ASJC Scopus subject areas

Access to Document

Other files and links

Fingerprint

Cite this