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dc.contributor.authorTaranovic, Aleksandar
dc.contributor.authorJevtic, Aleksandar
dc.contributor.authorTorras, Carme
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial
dc.contributor.otherInstitut de Robòtica i Informàtica Industrial
dc.date.accessioned2019-02-19T11:52:14Z
dc.date.available2019-02-19T11:52:14Z
dc.date.issued2018
dc.identifier.citationTaranovic, A.; Jevtic, A.; Torras, C. Adaptive modality selection algorithm in robot-assisted cognitive training. A: IEEE/RSJ International Conference on Intelligent Robots and Systems. "2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)". Institute of Electrical and Electronics Engineers (IEEE), 2018, p. 4456-4461.
dc.identifier.urihttp://hdl.handle.net/2117/129371
dc.description© 20xx IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.description.abstractInteraction of socially assistive robots with users is based on social cues coming from different interaction modalities, such as speech or gestures. However, using all modalities at all times may be inefficient as it can overload the user with redundant information and increase the task completion time. Additionally, users may favor certain modalities over the other as a result of their disability or personal preference. In this paper, we propose an Adaptive Modality Selection (AMS) algorithm that chooses modalities depending on the state of the user and the environment, as well as user preferences. The variables that describe the environment and the user state are defined as resources, and we posit that modalities are successful if certain resources possess specific values during their use. Besides the resources, the proposed algorithm takes into account user preferences which it learns while interacting with users. We tested our algorithm in simulations, and we implemented it on a robotic system that provides cognitive training, specifically Sequential memory exercises. Experimental results show that it is possible to use only a subset of available modalities without compromising the interaction. Moreover, we see a trend for users to perform better when interacting with a system with implemented AMS algorithm.
dc.format.extent6 p.
dc.language.isoeng
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Informàtica::Robòtica
dc.subject.otherRobots
dc.subject.otherSocial Human-Robot Interaction
dc.titleAdaptive modality selection algorithm in robot-assisted cognitive training
dc.typeConference report
dc.contributor.groupUniversitat Politècnica de Catalunya. ROBiri - Grup de Robòtica de l'IRI
dc.identifier.doi10.1109/IROS.2018.8593730
dc.description.peerreviewedPeer Reviewed
dc.subject.inspecClassificació INSPEC::Automation::Robots
dc.relation.publisherversionhttps://ieeexplore.ieee.org/document/8593730
dc.rights.accessOpen Access
drac.iddocument23931832
dc.description.versionPostprint (author's final draft)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/H2020/MSCA-ITN-721619
upcommons.citation.authorTaranovic, A.; Jevtic, A.; Torras, C.
upcommons.citation.contributorIEEE/RSJ International Conference on Intelligent Robots and Systems
upcommons.citation.publishedtrue
upcommons.citation.publicationName2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
upcommons.citation.startingPage4456
upcommons.citation.endingPage4461


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