Electromyographic Classification to Control the SPAR Glove

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dc.citation.firstpage244en_US
dc.citation.issueNumber20en_US
dc.citation.journalTitleIFAC-PapersOnLineen_US
dc.citation.lastpage250en_US
dc.citation.volumeNumber54en_US
dc.contributor.authorBritt, John E.en_US
dc.contributor.authorO'Malley, Marcia K.en_US
dc.contributor.authorRose, Chad G.en_US
dc.date.accessioned2022-04-13T14:42:07Zen_US
dc.date.available2022-04-13T14:42:07Zen_US
dc.date.issued2021en_US
dc.description.abstractThe SeptaPose Assistive and Rehabilitative (SPAR) Glove has been developed to assist individuals with upper extremity impairment arising from neuromuscular injury. The glove detects user intent via the MYO wearable electromyography (EMG) device. In this manuscript, pattern recognition tools infer the desired hand pose from EMG activity. The ability of the measurement and classification methods to distinguish between hand poses was evaluated with nine able-bodied participants and three participants with spinal cord injury (SCI) in an offline experiment. The strong performance of the proposed intent detection method is shown in the steady-state classification accuracy, presented as confusion matrices, as well as the average confidence for each classification. Building upon the strong performance in detecting pose, a pilot study with two participants with SCI presents the initial results of the real-time implementation of the system, which suggests directions for future work in improving the steady-state classification accuracy through expanded measurement and a refined taxonomy to enable intuitive control.en_US
dc.identifier.citationBritt, John E., O'Malley, Marcia K. and Rose, Chad G.. "Electromyographic Classification to Control the SPAR Glove." <i>IFAC-PapersOnLine,</i> 54, no. 20 (2021) Elsevier: 244-250. https://doi.org/10.1016/j.ifacol.2021.11.182.en_US
dc.identifier.doihttps://doi.org/10.1016/j.ifacol.2021.11.182en_US
dc.identifier.urihttps://hdl.handle.net/1911/112069en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsThis is an author's peer-reviewed final manuscript, as accepted by the publisher. The published article is copyrighted by the International Federation of Automatic Control.en_US
dc.subject.keywordAssistiveen_US
dc.subject.keywordRehabilitation Roboticsen_US
dc.subject.keywordRoboticsen_US
dc.subject.keywordMachine Learning in modelingen_US
dc.subject.keywordestimationen_US
dc.subject.keywordcontrolen_US
dc.titleElectromyographic Classification to Control the SPAR Gloveen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpost-printen_US
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