Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/12090
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dc.contributor.authorGaus, YFA-
dc.contributor.authorOlugbade, T-
dc.contributor.authorJan, A-
dc.contributor.authorQin, R-
dc.contributor.authorLiu, J-
dc.contributor.authorZhang, F-
dc.contributor.authorMeng, H-
dc.contributor.authorBianchi-Berthouze, N-
dc.date.accessioned2016-02-11T14:02:17Z-
dc.date.available2015-
dc.date.available2016-02-11T14:02:17Z-
dc.date.issued2015-
dc.identifier.citationProceedings of the 2015 ACM on International Conference on Multimodal Interaction, pp. 399 - 406, Seattle, USA, (9-13th November 2015)en_US
dc.identifier.isbn978-1-4503-3912-4-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/12090-
dc.identifier.urihttp://dl.acm.org/citation.cfm?id=2830599&preflayout=tabs-
dc.description.abstractTouch is a primary nonverbal communication channel used to communicate emotions or other social messages. Despite its importance, this channel is still very little explored in the affective computing field, as much more focus has been placed on visual and aural channels. In this paper, we investigate the possibility to automatically discriminate between different social touch types. We propose five distinct feature sets for describing touch behaviours captured by a grid of pressure sensors. These features are then combined together by using the Random Forest and Boosting methods for categorizing the touch gesture type. The proposed methods were evaluated on both the HAART (7 gesture types over different surfaces) and the CoST (14 gesture types over the same surface) datasets made available by the Social Touch Gesture Challenge 2015. Well above chance level performances were achieved with a 67% accuracy for the HAART and 59% for the CoST testing datasets respectively.en_US
dc.format.extent399 - 406-
dc.language.isoenen_US
dc.publisherACMen_US
dc.subjectSocial touchen_US
dc.subjectTouch gesture recognitionen_US
dc.subjectTouch featuresen_US
dc.titleSocial touch gesture recognition using random forest and boosting on distinct feature setsen_US
dc.typeConference Paperen_US
dc.identifier.doihttp://dx.doi.org/10.1145/2818346.2830599-
dc.relation.isPartOfProceedings of the 2015 ACM on International Conference on Multimodal Interaction-
pubs.noteslocation: Seattle, Washington, USA numpages: 8 acmid: 2830599 keywords: social touch, touch features, touch gesture recognition-
pubs.noteslocation: Seattle, Washington, USA numpages: 8 acmid: 2830599 keywords: social touch, touch features, touch gesture recognition-
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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