Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/12090
Title: Social touch gesture recognition using random forest and boosting on distinct feature sets
Authors: Gaus, YFA
Olugbade, T
Jan, A
Qin, R
Liu, J
Zhang, F
Meng, H
Bianchi-Berthouze, N
Keywords: Social touch;Touch gesture recognition;Touch features
Issue Date: 2015
Publisher: ACM
Citation: Proceedings of the 2015 ACM on International Conference on Multimodal Interaction, pp. 399 - 406, Seattle, USA, (9-13th November 2015)
Abstract: Touch 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.
URI: http://bura.brunel.ac.uk/handle/2438/12090
http://dl.acm.org/citation.cfm?id=2830599&preflayout=tabs
DOI: http://dx.doi.org/10.1145/2818346.2830599
ISBN: 978-1-4503-3912-4
Appears in Collections:Dept of Electronic and Computer Engineering Research Papers

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