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dc.contributor.authorUllauri, J. B.
dc.contributor.authorPetenel, L.
dc.contributor.authorUğurlu, Regaip Barkan
dc.contributor.authorYamada, Y.
dc.contributor.authorMorimoto, J.
dc.date.accessioned2016-02-17T11:05:45Z
dc.date.available2016-02-17T11:05:45Z
dc.date.issued2015
dc.identifier.isbn978-146737509-2
dc.identifier.urihttp://hdl.handle.net/10679/2876
dc.identifier.urihttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=7251472
dc.descriptionDue to copyright restrictions, the access to the full text of this article is only available via subscription.
dc.description.abstractExoskeletons are successful at supporting human motion only when the necessary amount of power is provided at the right time. Exoskeleton control based on EMG signals can be utilized to command the required amount of support in real-time. To this end, one needs to map human muscle activity to the desired task-specific exoskeleton torques. In order to achieve such mapping, this paper analyzes two distinct methods to estimate the human-elbow-joint torque based on the related muscle activity. The first model is adopted from pneumatic artificial muscles (PAMs). The second model is based on a machine learning method known as Gaussian Process Regression (GPR). The performance of both approaches were assessed based on their ability to estimate the elbow-joint torque of two able-bodied subjects using EMG signals that were collected from biceps and triceps muscles. The experiments suggest that the GPR-based approach provides relatively more favorable predictions.
dc.description.sponsorshipImPACT Program of Council for Science Technology and Innovation ; NEDO ; SRPBS of the MEXT ; MEXT KAKENHI ; JST-SICP ; MIC-SCOPE ; JSPS-MIZS ; Slovene Human Resources Development and Scholarship Fund.
dc.language.isoengen_US
dc.publisherIEEE
dc.relation.ispartofAdvanced Robotics (ICAR), 2015 International Conference on
dc.rightsrestrictedAccess
dc.titleOn the EMG-based torque estimation for humans coupled with a force-controlled elbow exoskeletonen_US
dc.typeConference paperen_US
dc.peerreviewedyes
dc.publicationstatuspublisheden_US
dc.contributor.departmentÖzyeğin University
dc.contributor.authorID241209
dc.contributor.authorID0000-0002-9124-7441
dc.contributor.ozuauthorUğurlu, Regaip Barkan
dc.identifier.startpage302
dc.identifier.endpage307
dc.identifier.wosWOS:000380471000048
dc.identifier.doi10.1109/ICAR.2015.7251472
dc.subject.keywordsHuman torque prediction
dc.subject.keywordsEMG
dc.subject.keywordsGPR
dc.subject.keywordsPAM model
dc.identifier.scopusSCOPUS:2-s2.0-84957666739
dc.contributor.authorMale1


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