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dc.contributor.authorJi, Z.
dc.contributor.authorAmirabdollahian, F.
dc.contributor.authorPolani, D.
dc.contributor.authorDautenhahn, K.
dc.date.accessioned2013-01-14T11:59:20Z
dc.date.available2013-01-14T11:59:20Z
dc.date.issued2011
dc.identifier.citationJi , Z , Amirabdollahian , F , Polani , D & Dautenhahn , K 2011 , Histogram based classification of tactile patterns on periodically distributed skin sensors for a humanoid robot . in Procs IEEE International Workshop on Robot and Human Interactive Communication : RO-MAN 2011 . IEEE , pp. 433-440 , RO-MAN 2011 , Atlanta , United States , 31/07/11 . https://doi.org/10.1109/ROMAN.2011.6005261
dc.identifier.citationconference
dc.identifier.isbn978-1-4577-1571-6
dc.identifier.isbn978-1-4577-1572-3
dc.identifier.otherPURE: 457686
dc.identifier.otherPURE UUID: eb68b509-25fa-48e1-a1bf-a2844b07f00e
dc.identifier.otherScopus: 80053009731
dc.identifier.urihttp://hdl.handle.net/2299/9610
dc.description.abstractThe main target of this work is to improve human-robot interaction capabilities, by adding a new modality of sense, touch, to KASPAR, a humanoid robot. Large scale distributed skin-like sensors are designed and integrated on the robot, covering KASPAR at various locations. One of the challenges is to classify different types of touch. Unlike digital images represented by grids of pixels, the geometrical structure of the sensor array limits the capability of straightforward application of well-established approaches for image patterns. This paper introduces a novel histogram-based classification algorithm, transforming tactile data into histograms of local features termed as codebook. Tactile pattern can be invariant at periodical locations, allowing tactile pattern classification using a smaller number of training data, instead of using training data from everywhere on the large scale skin sensors. To generate the codebook, this method uses a two-layer approach, namely local neighbourhood structures and encodings of pressure distribution of the local neighbourhood. Classification is performed based on the constructed features using Support Vector Machine (SVM) with the intersection kernel. Real experimental data are used for experiment to classify different patterns and have shown promising accuracy. To evaluate the performance, it is also compared with the SVM using the Radial Basis Function (RBF) kernel and results are discussed from both aspects of accuracy and the location invariance property.en
dc.format.extent8
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofProcs IEEE International Workshop on Robot and Human Interactive Communication
dc.titleHistogram based classification of tactile patterns on periodically distributed skin sensors for a humanoid roboten
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionAdaptive Systems
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=80053009731&partnerID=8YFLogxK
dc.relation.schoolSchool of Computer Science
dcterms.dateAccepted2011
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1109/ROMAN.2011.6005261
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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