Open AccessSocial Touch Recognition Based on Support Vector Machine and T-Distributed Stochastic Neighbour Embedding as Pre-processing
Author(s) -
Ahmed Abbas,
Adil I. Khalil,
Sameera A. Abdul-Kader
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1076/1/012042
Subject(s) - gesture , modality (human–computer interaction) , embedding , computer science , preprocessor , support vector machine , gesture recognition , artificial intelligence , modalities , pattern recognition (psychology) , field (mathematics) , speech recognition , computer vision , mathematics , social science , sociology , pure mathematics
Today, one of important field is social touch gesture recognition for touch modality, which can lead to highly efficient and realistic human–robot interaction. Touch is an important interaction modality in social interaction, for instance touch can communicate emotions and can intensify emotions communicated by other modalities. In this paper, the touch gesture recognition is performed using a dataset that previously measured for numerous subjects that perform various social gestures. This dataset is dubbed as the corpus of social touch (CoST), where touches were performed on a mannequin arm. The T-Distributed Stochastic Neighbor Embedding (T-SNE) algorithm is used to reduce the dimensions of the input data. The T-SNE algorithm was used as a preprocessing stage before classification operations. The output of the T-SNE is fed to the support vector machine (SVM). The performance of the proposed systems was evaluated using leave-one-subject-out cross-validation method. The range of recognition results 31.6% to 81.4%, Mean = 61.7% and Standard Deviation = 10.05%. The proposed method can recognize gestures in nearly real time after acquiring a minimum number of frames (629 ms). which is comparable with the results of Jung et al.