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Social Touch Gesture Recognition using Random Forest and Boosting on Distinct Feature Sets

Gaus, YFA; Olugbade, T; Jan, A; Qin, R; Liu, J; Zhang, F; Meng, H; (2015) Social Touch Gesture Recognition using Random Forest and Boosting on Distinct Feature Sets. In: Zhang, Z and Cohen, P and Bohus, D and Horaud, R, (eds.) Proceedings of the 2015 ACM on International Conference on Multimodal Interaction. (pp. pp. 399-406). Association for Computing Machinery: Seattle, WA, USA. Green open access

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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.

Type: Proceedings paper
Title: Social Touch Gesture Recognition using Random Forest and Boosting on Distinct Feature Sets
Event: 17th International Conference on Multimodal Interaction, Seattle, Washington, USA - November 09 - 13, 2015
Location: Seattle, WA
Dates: 09 November 2015 - 13 November 2015
Open access status: An open access version is available from UCL Discovery
DOI: 10.1145/2818346.2830599
Publisher version: http://dx.doi.org/10.1145/2818346.2830599
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Science & technology, technology, computer science, theory & methods, engineering, electrical & electronic, computer science, engineering, social touch, touch gesture recognition, touch features, huggable robot probo.
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > UCL Interaction Centre
URI: https://discovery.ucl.ac.uk/id/eprint/1476182
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