SEMG classification for upper-limb prosthesis control using higher order statistics

Alireza Khadivi; Kianoush Nazarpour; Hamid Soltanain Zadeh

doi:10.1109/ICASSP.2005.1416321

SEMG classification for upper-limb prosthesis control using higher order statistics

Alireza Khadivi
, Kianoush Nazarpour
, Hamid Soltanain Zadeh

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

Abstract

The aim of this paper is to present application of higher order statistics for surface electromyogram (sEMG) signal pattern classification. The new pattern recognition algorithm exploits a multilayer perceptron (MLP) as the classifier and the feature vector is a combination of cumulants of the second-, third- and fourth- orders and integral of absolute (IAV) of two channel sEMG stationary segments. The detected sEMG signals are used in classifying four upper-limb primitive motions, namely, elbow flexion (F), elbow extension (E), wrist supination (S) and wrist pronation (P). The simulation results illustrate the considerable accuracy of the proposed framework in sEMG pattern recognition.

Original language	English
Title of host publication	Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.
Publisher	Institute of Electrical and Electronics Engineers
Pages	385-388
Number of pages	4
Volume	5
ISBN (Print)	0-7803-8874-7
DOIs	https://doi.org/10.1109/ICASSP.2005.1416321
Publication status	Published - 9 May 2005
Event	IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '05) - Philadelphia, United States Duration: 18 Mar 2005 → 23 Mar 2005

Publication series

Name
Publisher	IEEE
ISSN (Print)	1520-6149
ISSN (Electronic)	2379-190X

Conference

Conference	IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '05)
Country/Territory	United States
City	Philadelphia
Period	18/03/05 → 23/03/05

Access to Document

10.1109/ICASSP.2005.1416321Licence: All Rights Reserved

https://ieeexplore.ieee.org/abstract/document/1416321Licence: All Rights Reserved

Cite this

@inproceedings{08bbf816de384f6386fb9ad0b9bafb30,

title = "SEMG classification for upper-limb prosthesis control using higher order statistics",

abstract = "The aim of this paper is to present application of higher order statistics for surface electromyogram (sEMG) signal pattern classification. The new pattern recognition algorithm exploits a multilayer perceptron (MLP) as the classifier and the feature vector is a combination of cumulants of the second-, third- and fourth- orders and integral of absolute (IAV) of two channel sEMG stationary segments. The detected sEMG signals are used in classifying four upper-limb primitive motions, namely, elbow flexion (F), elbow extension (E), wrist supination (S) and wrist pronation (P). The simulation results illustrate the considerable accuracy of the proposed framework in sEMG pattern recognition.",

author = "Alireza Khadivi and Kianoush Nazarpour and Zadeh, \{Hamid Soltanain\}",

year = "2005",

month = may,

day = "9",

doi = "10.1109/ICASSP.2005.1416321",

language = "English",

isbn = "0-7803-8874-7",

volume = "5",

publisher = "Institute of Electrical and Electronics Engineers",

pages = "385--388",

booktitle = "Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.",

address = "United States",

note = "IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '05) ; Conference date: 18-03-2005 Through 23-03-2005",

}

Khadivi, A, Nazarpour, K & Zadeh, HS 2005, SEMG classification for upper-limb prosthesis control using higher order statistics. in Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.. vol. 5, Institute of Electrical and Electronics Engineers, pp. 385-388, IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '05), Philadelphia, Pennsylvania, United States, 18/03/05. https://doi.org/10.1109/ICASSP.2005.1416321

SEMG classification for upper-limb prosthesis control using higher order statistics. / Khadivi, Alireza; Nazarpour, Kianoush; Zadeh, Hamid Soltanain.
Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.. Vol. 5 Institute of Electrical and Electronics Engineers, 2005. p. 385-388.

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

TY - GEN

T1 - SEMG classification for upper-limb prosthesis control using higher order statistics

AU - Khadivi, Alireza

AU - Nazarpour, Kianoush

AU - Zadeh, Hamid Soltanain

PY - 2005/5/9

Y1 - 2005/5/9

N2 - The aim of this paper is to present application of higher order statistics for surface electromyogram (sEMG) signal pattern classification. The new pattern recognition algorithm exploits a multilayer perceptron (MLP) as the classifier and the feature vector is a combination of cumulants of the second-, third- and fourth- orders and integral of absolute (IAV) of two channel sEMG stationary segments. The detected sEMG signals are used in classifying four upper-limb primitive motions, namely, elbow flexion (F), elbow extension (E), wrist supination (S) and wrist pronation (P). The simulation results illustrate the considerable accuracy of the proposed framework in sEMG pattern recognition.

AB - The aim of this paper is to present application of higher order statistics for surface electromyogram (sEMG) signal pattern classification. The new pattern recognition algorithm exploits a multilayer perceptron (MLP) as the classifier and the feature vector is a combination of cumulants of the second-, third- and fourth- orders and integral of absolute (IAV) of two channel sEMG stationary segments. The detected sEMG signals are used in classifying four upper-limb primitive motions, namely, elbow flexion (F), elbow extension (E), wrist supination (S) and wrist pronation (P). The simulation results illustrate the considerable accuracy of the proposed framework in sEMG pattern recognition.

U2 - 10.1109/ICASSP.2005.1416321

DO - 10.1109/ICASSP.2005.1416321

M3 - Conference contribution

SN - 0-7803-8874-7

VL - 5

SP - 385

EP - 388

BT - Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005.

PB - Institute of Electrical and Electronics Engineers

T2 - IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '05)

Y2 - 18 March 2005 through 23 March 2005

ER -

SEMG classification for upper-limb prosthesis control using higher order statistics

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