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Towards Low-Dimensionsal Proportional Myoelectric Control

Lookup NU author(s): Dr Agamemnon Krasoulis, Professor Kianoush Nazarpour



One way of enhancing the dexterity of powered myoelectric prostheses is via proportional and simultaneous control of multiple degrees-of-freedom (DOFs). Recently, it has been demonstrated that the reconstruction of finger movement is feasible by using features of the surface electromyogram (sEMG) signal. In such paradigms, the number of predictors and target variables is usually large, and strong correlations are present in both the input and output domains. Synergistic patterns in the sEMG space have been previously exploited to facilitate kinematics decoding. In this work, we propose a framework for simultaneous input-output dimensionality reduction based on the generalized eigenvalue problem formulation of multiple linear regression (MLR). We demonstrate that the proposed methodology outperforms simultaneous input-output dimensionality reduction based on principal component analysis (PCA), while the prediction accuracy of the full rank regression (FRR) method can be achieved by using only a few relevant dimensions.

Publication metadata

Author(s): Krasoulis A, Nazarpour K, Vijayakumar S

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

Year of Conference: 2015

Pages: 7155-7158

Online publication date: 05/11/2015

Acceptance date: 25/08/2015

Date deposited: 29/01/2018

ISSN: 1558-4615

Publisher: IEEE


DOI: 10.1109/EMBC.2015.7320042

Library holdings: Search Newcastle University Library for this item

ISBN: 9781424492718