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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.
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
URL: http://doi.org/10.1109/NER.2015.7146702
DOI: 10.1109/NER.2015.7146702
Library holdings: Search Newcastle University Library for this item
ISBN: 9781467363891