On Muscle Selection for EMG Based Decoding of Dexterous, In-Hand Manipulation Motions

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dc.contributor.author Kwon, Y en
dc.contributor.author Dwivedi, Anany en
dc.contributor.author McDaid, Andrew en
dc.contributor.author Liarokapis, Minas en
dc.coverage.spatial Hawaii, USA en
dc.date.accessioned 2019-02-26T22:48:15Z en
dc.date.issued 2018-07-17 en
dc.identifier.citation Proceedings 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). IEEE. 1672-1675. 17 Jul 2018 en
dc.identifier.isbn 978-1-5386-3646-6 en
dc.identifier.uri http://hdl.handle.net/2292/45518 en
dc.description.abstract The field of Brain Machine Interfaces (BMI) has attracted an increased interest due to its multiple applications in the health and entertainment domains. A BMI enables a direct interface between the brain and machines and is capable of translating neuronal information into meaningful actions (e.g., Electromyography based control of a prosthetic hand). One of the biggest challenges in developing a surface Electromyography (sEMG) based interface is the selection of the right muscles for the execution of a desired task. In this work, we investigate optimal muscle selections for sEMG based decoding of dexterous in-hand manipulation motions. To do that, we use EMG signals derived from 14 muscle sites of interest (7 on the hand and 7 on the forearm) and an optical motion capture system that records the object motion. The regression problem is formulated using the Random Forests methodology that is based on decision trees. Regarding features selection, we use the following time-domain features: root mean square, waveform length and zero crossings. A 5-fold cross validation procedure is used for model assessment purposes and the importance values are calculated for each feature. This pilot study shows that the muscles of the hand contribute more than the muscles of the forearm to the execution of inhand manipulation tasks and that the myoelectric activations of the hand muscles provide better estimation accuracies for the decoding of manipulation motions. These outcomes suggest that the loss of the hand muscles in certain amputations limits the amputees' ability to perform a dexterous, EMG based control of a prosthesis in manipulation tasks. The results discussed can also be used for improving the efficiency and intuitiveness of EMG based interfaces for healthy subjects. en
dc.publisher IEEE en
dc.relation.ispartof 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society en
dc.relation.ispartofseries Proceedings 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.rights.uri https://www.ieee.org/publications/rights/author-posting-policy.html en
dc.title On Muscle Selection for EMG Based Decoding of Dexterous, In-Hand Manipulation Motions en
dc.type Conference Item en
dc.identifier.doi 10.1109/EMBC.2018.8512624 en
pubs.begin-page 1672 en
dc.rights.holder Copyright: IEEE en
pubs.end-page 1675 en
pubs.finish-date 2018-07-21 en
pubs.start-date 2018-07-18 en
dc.rights.accessrights http://purl.org/eprint/accessRights/OpenAccess en
pubs.subtype Proceedings en
pubs.elements-id 751502 en
pubs.org-id Engineering en
pubs.org-id Mechanical Engineering en
dc.identifier.eissn 1558-4615 en
pubs.record-created-at-source-date 2018-08-14 en
pubs.dimensions-id 30440716 en


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