dc.contributor.author | Imatz-Ojanguren, Eukene | |
dc.contributor.author | Irigoyen, Eloy | |
dc.contributor.author | Keller, Thierry | |
dc.date.accessioned | 2016-11-24T13:20:54Z | |
dc.date.available | 2016-11-24T13:20:54Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | International Joint Conference SOCO’16-CISIS’16-ICEUTE’16, Volume 527 of the series Advances in Intelligent Systems and Computing pp 313-322 | en |
dc.identifier.isbn | 978-3-319-47363-5 | en |
dc.identifier.issn | 2194-5357 | en |
dc.identifier.uri | http://hdl.handle.net/11556/344 | |
dc.description.abstract | Hand 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.iso | eng | en |
dc.publisher | Springer International Publishing | en |
dc.title | Reinforcement Learning for Hand Grasp with Surface Multi-field Neuroprostheses | en |
dc.type | conferenceObject | en |
dc.identifier.doi | 10.1007/978-3-319-47364-2_30 | en |
dc.isi | No | en |
dc.rights.accessRights | embargoedAccess | en |
dc.subject.keywords | Neuroprostheses | en |
dc.subject.keywords | Functional electrical stimulation | en |
dc.subject.keywords | Grasp | en |
dc.subject.keywords | Reinforcement learning | en |
dc.subject.keywords | Modeling and control | en |
dc.journal.title | Advances in Intelligent Systems and Computing | en |
dc.page.final | 322 | en |
dc.page.initial | 313 | en |
dc.volume.number | 527 | en |
dc.identifier.esbn | 978-3-319-47364-2 | en |