Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis : a review

Ganesh R. Naik; Sridhar Arjunan; Dinesh Kumar

doi:10.1007/s13246-011-0066-4

Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis : a review

Ganesh R. Naik, Sridhar Arjunan, Dinesh Kumar

Western Sydney University

Research output: Contribution to journal › Article › peer-review

37 Citations (Scopus)

Abstract

The surface electromyography (sEMG) signal separation and decphompositions has always been an interesting research topic in the field of rehabilitation and medical research. Subtle myoelectric control is an advanced technique concerned with the detection, processing, classification, and application of myoelectric signals to control human-assisting robots or rehabilitation devices. This paper reviews recent research and development in independent component analysis and Fractal dimensional analysis for sEMG pattern recognition, and presents state-of-the-art achievements in terms of their type, structure, and potential application. Directions for future research are also briefly outlined.

Original language	English
Pages (from-to)	179-193
Number of pages	15
Journal	Australasian Physical and Engineering Sciences in Medicine
Volume	34
Issue number	2
DOIs	https://doi.org/10.1007/s13246-011-0066-4
Publication status	Published - 2011

Keywords

blind source separation
electromyography
fractal analysis
fractals
independent component analysis

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10.1007/s13246-011-0066-4

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abstract = "The surface electromyography (sEMG) signal separation and decphompositions has always been an interesting research topic in the field of rehabilitation and medical research. Subtle myoelectric control is an advanced technique concerned with the detection, processing, classification, and application of myoelectric signals to control human-assisting robots or rehabilitation devices. This paper reviews recent research and development in independent component analysis and Fractal dimensional analysis for sEMG pattern recognition, and presents state-of-the-art achievements in terms of their type, structure, and potential application. Directions for future research are also briefly outlined.",

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AU - Arjunan, Sridhar

AU - Kumar, Dinesh

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Y1 - 2011

N2 - The surface electromyography (sEMG) signal separation and decphompositions has always been an interesting research topic in the field of rehabilitation and medical research. Subtle myoelectric control is an advanced technique concerned with the detection, processing, classification, and application of myoelectric signals to control human-assisting robots or rehabilitation devices. This paper reviews recent research and development in independent component analysis and Fractal dimensional analysis for sEMG pattern recognition, and presents state-of-the-art achievements in terms of their type, structure, and potential application. Directions for future research are also briefly outlined.

AB - The surface electromyography (sEMG) signal separation and decphompositions has always been an interesting research topic in the field of rehabilitation and medical research. Subtle myoelectric control is an advanced technique concerned with the detection, processing, classification, and application of myoelectric signals to control human-assisting robots or rehabilitation devices. This paper reviews recent research and development in independent component analysis and Fractal dimensional analysis for sEMG pattern recognition, and presents state-of-the-art achievements in terms of their type, structure, and potential application. Directions for future research are also briefly outlined.

KW - blind source separation

KW - electromyography

KW - fractal analysis

KW - fractals

KW - independent component analysis

UR - http://handle.westernsydney.edu.au:8081/1959.7/uws:42792

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JO - Australasian Physical and Engineering Sciences in Medicine

JF - Australasian Physical and Engineering Sciences in Medicine

IS - 2

ER -

Applications of ICA and fractal dimension in sEMG signal processing for subtle movement analysis : a review

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Keywords

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