Please use this identifier to cite or link to this item: http://hdl.handle.net/10316/93187
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dc.contributor.authorFaria, Diego-
dc.contributor.authorMartins, Ricardo Filipe Alves-
dc.contributor.authorDias, Jorge-
dc.date.accessioned2021-02-23T22:47:01Z-
dc.date.available2021-02-23T22:47:01Z-
dc.date.issued2009-07-28-
dc.identifier.urihttp://hdl.handle.net/10316/93187-
dc.description.abstractIn this work we present a general structure of an initial Bayesian framework to describe the mechanisms underlying the human strategies that define the appropriate characteristics of the reach-to-grasp movements to specific contexts, objects and how these strategies can be extended and replicated to other contexts and objects. The Bayesian framework uses information extracted from data about the pose of the hand, fingers and head acquired by a magnetic tracker device, finger flexure data acquired by a data glove, as well as, data about eye gaze and saccade movements of the subject.pt
dc.language.isoengpt
dc.rightsopenAccesspt
dc.titleHuman reach-to-grasp generalization strategies: a Bayesian approachpt
dc.typeconferenceObjectpt
degois.publication.titleRSS 2009 - Robotics: Science and Systems Session - Workshop: Understanding the Human Hand for Advancing Robotic Manipulationpt
dc.peerreviewedyespt
dc.identifier.doi10.5281/zenodo.4553390-
dc.date.embargo2009-07-28*
uc.date.periodoEmbargo0pt
item.grantfulltextopen-
item.languageiso639-1en-
item.fulltextCom Texto completo-
crisitem.author.orcid0000-0001-7184-185X-
Appears in Collections:FCTUC Eng.Electrotécnica - Artigos em Livros de Actas
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