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Linear and Nonlinear Regression Techniques for Simultaneous and Proportional Myoelectric Control

Authors
Hahne, J. M.Biessmann, F.Jiang, N.Rehbaum, H.Farina, D.Meinecke, F. C.Mueller, K. -R.Parra, L. C.
Issue Date
3월-2014
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Keywords
Amputee; electromyography (EMG); hand prostheses; regression; simultaneous myoelectric control
Citation
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, v.22, no.2, pp.269 - 279
Indexed
SCIE
SCOPUS
Journal Title
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
Volume
22
Number
2
Start Page
269
End Page
279
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/99093
DOI
10.1109/TNSRE.2014.2305520
ISSN
1534-4320
Abstract
In recent years the number of active controllable joints in electrically powered hand-prostheses has increased significantly. However, the control strategies for these devices in current clinical use are inadequate as they require separate and sequential control of each degree-of-freedom (DoF). In this study we systematically compare linear and nonlinear regression techniques for an independent, simultaneous and proportional myoelectric control of wrist movements with two DoF. These techniques include linear regression, mixture of linear experts (ME), multilayer-perceptron, and kernel ridge regression (KRR). They are investigated offline with electro-myographic signals acquired from ten able-bodied subjects and one person with congenital upper limb deficiency. The control accuracy is reported as a function of the number of electrodes and the amount and diversity of training data providing guidance for the requirements in clinical practice. The results showed that KRR, a nonparametric statistical learning method, outperformed the other methods. However, simple transformations in the feature space could linearize the problem, so that linear models could achieve similar performance as KRR at much lower computational costs. Especially ME, a physiologically inspired extension of linear regression represents a promising candidate for the next generation of prosthetic devices.
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