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dc.contributor.authorImatz-Ojanguren, Eukene
dc.contributor.authorIrigoyen, Eloy
dc.contributor.authorKeller, Thierry
dc.date.accessioned2016-11-24T13:20:54Z
dc.date.available2016-11-24T13:20:54Z
dc.date.issued2017
dc.identifier.citationInternational Joint Conference SOCO’16-CISIS’16-ICEUTE’16, Volume 527 of the series Advances in Intelligent Systems and Computing pp 313-322en
dc.identifier.isbn978-3-319-47363-5en
dc.identifier.issn2194-5357en
dc.identifier.urihttp://hdl.handle.net/11556/344
dc.description.abstractHand grasp is a complex system that plays an important role in the activities of daily living. Upper-limb neuroprostheses aim at restor- ing lost reaching and grasping functions on people su ering from neural disorders. However, the dimensionality and complexity of the upper-limb makes the neuroprostheses modeling and control challenging. In this work we present preliminary results for checking the feasibility of using a re- inforcement learning (RL) approach for achieving grasp functions with a surface multi- eld neuroprosthesis for grasping. Grasps from 20 healthy subjects were recorded to build a reference for the RL system and then two di erent award strategies were tested on simulations based on neuro- fuzzy models of hemiplegic patients. These rst results suggest that RL might be a possible solution for obtaining grasp function by means of multi- eld neuroprostheses in the near future.en
dc.language.isoengen
dc.publisherSpringer International Publishingen
dc.titleReinforcement Learning for Hand Grasp with Surface Multi-field Neuroprosthesesen
dc.typeconferenceObjecten
dc.identifier.doi10.1007/978-3-319-47364-2_30en
dc.isiNoen
dc.rights.accessRightsembargoedAccessen
dc.subject.keywordsNeuroprosthesesen
dc.subject.keywordsFunctional electrical stimulationen
dc.subject.keywordsGraspen
dc.subject.keywordsReinforcement learningen
dc.subject.keywordsModeling and controlen
dc.journal.titleAdvances in Intelligent Systems and Computingen
dc.page.final322en
dc.page.initial313en
dc.volume.number527en
dc.identifier.esbn978-3-319-47364-2en


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