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Evaluation of Regression Methods for the Continuous Decoding of Finger Movement from Surface EMG and Accelerometry

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



The reconstruction of finger movement activity from surface electromyography (sEMG) has been proposed for the proportional and simultaneous myoelectric control of multiple degrees-of-freedom (DOFs). In this paper, we propose a framework for assessing decoding performance on novel movements, that is movements not included in the training dataset. We then use our proposed framework to compare the performance of linear and kernel ridge regression for the reconstruction of finger movement from sEMG and accelerometry. Our findings provide evidence that, although the performance of the non-linear method is superior for movements seen by the decoder during the training phase, the performance of the two algorithms is comparable when generalizing to novel movements.

Publication metadata

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

Publication type: Conference Proceedings (inc. Abstract)

Publication status: Published

Conference Name: 2015 7th International IEEE/EMBS Conference on Neural Engineering, NER

Year of Conference: 2015

Pages: 631-634

Print publication date: 01/01/2015

Online publication date: 02/07/2015

Acceptance date: 01/01/2015

Date deposited: 29/01/2018

Publisher: IEEE


DOI: 10.1109/NER.2015.7146702

Library holdings: Search Newcastle University Library for this item

ISBN: 9781467363891