Fractal and twin SVM-based handgrip recognition for healthy subjects and trans-radial amputees using myoelectric signal

Fractal and twin SVM-based handgrip recognition for healthy subjects and trans-radial amputees... AbstractIdentifying functional handgrip patterns using surface electromygram (sEMG) signal recorded from amputee residual muscle is required for controlling the myoelectric prosthetic hand. In this study, we have computed the signal fractal dimension (FD) and maximum fractal length (MFL) during different grip patterns performed by healthy and transradial amputee subjects. The FD and MFL of the sEMG, referred to as the fractal features, were classified using twin support vector machines (TSVM) to recognize the handgrips. TSVM requires fewer support vectors, is suitable for data sets with unbalanced distributions, and can simultaneously be trained for improving both sensitivity and specificity. When compared with other methods, this technique resulted in improved grip recognition accuracy, sensitivity, and specificity, and this improvement was significant (κ=0.91). http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biomedical Engineering / Biomedizinische Technik de Gruyter

Fractal and twin SVM-based handgrip recognition for healthy subjects and trans-radial amputees using myoelectric signal

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Publisher
De Gruyter
Copyright
©2016 by De Gruyter
ISSN
1862-278X
eISSN
1862-278X
D.O.I.
10.1515/bmt-2014-0134
Publisher site
See Article on Publisher Site

Abstract

AbstractIdentifying functional handgrip patterns using surface electromygram (sEMG) signal recorded from amputee residual muscle is required for controlling the myoelectric prosthetic hand. In this study, we have computed the signal fractal dimension (FD) and maximum fractal length (MFL) during different grip patterns performed by healthy and transradial amputee subjects. The FD and MFL of the sEMG, referred to as the fractal features, were classified using twin support vector machines (TSVM) to recognize the handgrips. TSVM requires fewer support vectors, is suitable for data sets with unbalanced distributions, and can simultaneously be trained for improving both sensitivity and specificity. When compared with other methods, this technique resulted in improved grip recognition accuracy, sensitivity, and specificity, and this improvement was significant (κ=0.91).

Journal

Biomedical Engineering / Biomedizinische Technikde Gruyter

Published: Feb 1, 2016

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