Feature selection for myoelectric pattern recognition using two channel surface electromyography signals

Omkar S. Powar; Krishnan Chemmangat

doi:10.1109/TENCON.2017.8228007

Feature selection for myoelectric pattern recognition using two channel surface electromyography signals

Omkar S. Powar, Krishnan Chemmangat

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

5 Citations (Scopus)

Abstract

Pattern recognition scheme is used for discriminating various classes of hand motion with feature extracted from the surface electromyography signals. However, while using a relatively large feature set for classification process, the computational complexity increases tremendously. To overcome this, the paper implements feature selection technique using wrapper evaluation and four different search methods without significantly affecting the classification accuracy. The performance of the features is tested on surface electromyography data collected from seven subjects, with eight classes of movements. Practical results indicate that using feature selection methods can achieve the same accuracy with lesser number of features.

Original language	English
Title of host publication	TENCON 2017 - 2017 IEEE Region 10 Conference
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1022-1026
Number of pages	5
ISBN (Electronic)	9781509011339
DOIs	https://doi.org/10.1109/TENCON.2017.8228007
Publication status	Published - 19-12-2017
Event	2017 IEEE Region 10 Conference, TENCON 2017 - Penang, Malaysia Duration: 05-11-2017 → 08-11-2017

Publication series

Name	IEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume	2017-December
ISSN (Print)	2159-3442
ISSN (Electronic)	2159-3450

Conference

Conference	2017 IEEE Region 10 Conference, TENCON 2017
Country/Territory	Malaysia
City	Penang
Period	05-11-17 → 08-11-17

All Science Journal Classification (ASJC) codes

Computer Science Applications
Electrical and Electronic Engineering

Access to Document

10.1109/TENCON.2017.8228007

Cite this

Powar, O. S., & Chemmangat, K. (2017). Feature selection for myoelectric pattern recognition using two channel surface electromyography signals. In TENCON 2017 - 2017 IEEE Region 10 Conference (pp. 1022-1026). (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2017-December). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/TENCON.2017.8228007

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title = "Feature selection for myoelectric pattern recognition using two channel surface electromyography signals",

abstract = "Pattern recognition scheme is used for discriminating various classes of hand motion with feature extracted from the surface electromyography signals. However, while using a relatively large feature set for classification process, the computational complexity increases tremendously. To overcome this, the paper implements feature selection technique using wrapper evaluation and four different search methods without significantly affecting the classification accuracy. The performance of the features is tested on surface electromyography data collected from seven subjects, with eight classes of movements. Practical results indicate that using feature selection methods can achieve the same accuracy with lesser number of features.",

author = "Powar, \{Omkar S.\} and Krishnan Chemmangat",

note = "Funding Information: ACKNOWLEDGMENT This work was supported by the Visvesvaraya Phd Scheme for Electronics \& IT, Government of India. Publisher Copyright: {\textcopyright} 2017 IEEE.; 2017 IEEE Region 10 Conference, TENCON 2017 ; Conference date: 05-11-2017 Through 08-11-2017",

year = "2017",

month = dec,

day = "19",

doi = "10.1109/TENCON.2017.8228007",

language = "English",

series = "IEEE Region 10 Annual International Conference, Proceedings/TENCON",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1022--1026",

booktitle = "TENCON 2017 - 2017 IEEE Region 10 Conference",

address = "United States",

}

Powar, OS & Chemmangat, K 2017, Feature selection for myoelectric pattern recognition using two channel surface electromyography signals. in TENCON 2017 - 2017 IEEE Region 10 Conference. IEEE Region 10 Annual International Conference, Proceedings/TENCON, vol. 2017-December, Institute of Electrical and Electronics Engineers Inc., pp. 1022-1026, 2017 IEEE Region 10 Conference, TENCON 2017, Penang, Malaysia, 05-11-17. https://doi.org/10.1109/TENCON.2017.8228007

Feature selection for myoelectric pattern recognition using two channel surface electromyography signals. / Powar, Omkar S.; Chemmangat, Krishnan.
TENCON 2017 - 2017 IEEE Region 10 Conference. Institute of Electrical and Electronics Engineers Inc., 2017. p. 1022-1026 (IEEE Region 10 Annual International Conference, Proceedings/TENCON; Vol. 2017-December).

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

TY - GEN

T1 - Feature selection for myoelectric pattern recognition using two channel surface electromyography signals

AU - Powar, Omkar S.

AU - Chemmangat, Krishnan

PY - 2017/12/19

Y1 - 2017/12/19

N2 - Pattern recognition scheme is used for discriminating various classes of hand motion with feature extracted from the surface electromyography signals. However, while using a relatively large feature set for classification process, the computational complexity increases tremendously. To overcome this, the paper implements feature selection technique using wrapper evaluation and four different search methods without significantly affecting the classification accuracy. The performance of the features is tested on surface electromyography data collected from seven subjects, with eight classes of movements. Practical results indicate that using feature selection methods can achieve the same accuracy with lesser number of features.

AB - Pattern recognition scheme is used for discriminating various classes of hand motion with feature extracted from the surface electromyography signals. However, while using a relatively large feature set for classification process, the computational complexity increases tremendously. To overcome this, the paper implements feature selection technique using wrapper evaluation and four different search methods without significantly affecting the classification accuracy. The performance of the features is tested on surface electromyography data collected from seven subjects, with eight classes of movements. Practical results indicate that using feature selection methods can achieve the same accuracy with lesser number of features.

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T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON

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PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2017 IEEE Region 10 Conference, TENCON 2017

Y2 - 5 November 2017 through 8 November 2017

ER -

Feature selection for myoelectric pattern recognition using two channel surface electromyography signals

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