Paper Info
Title
Social Touch Gesture Recognition using Random Forest and Boosting on Distinct Feature Sets
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.
Year
DOI
Venue
2015
10.1145/2818346.2830599
ACM International Conference on Multimodal Interaction
Keywords
Field
DocType
Social Touch, Touch Gesture Recognition, Touch Features
Computer vision,Computer science,Gesture,Gesture recognition,Communication channel,Nonverbal communication,Human–computer interaction,Artificial intelligence,Boosting (machine learning),Affective computing,Random forest,Grid
Conference
Citations 
PageRank 
References 
7
0.50
31
Authors
8
Name
Order
Citations
PageRank
Yona Falinie A. Gaus1403.87
Temitayo A. Olugbade2446.14
Asim Jan3513.43
Rui Qin4101.91
Jingxin Liu5121.30
Fan Zhang670.84
Hongying Meng783269.39
Nadia Bianchi-Berthouze8123998.61